793
13
Livelihoods and Poverty
Coordinating Lead Authors:
Lennart Olsson (Sweden), Maggie Opondo (Kenya), Petra Tschakert (USA)
Lead Authors:
Arun Agrawal (USA), Siri H. Eriksen (Norway), Shiming Ma (China), Leisa N. Perch (Barbados),
Sumaya A. Zakieldeen (Sudan)
Contributing Authors:
Catherine Jampel (USA), Eric Kissel (USA), Valentina Mara (Romania), Andrei Marin (Norway),
David Satterthwaite (UK), Asuncion Lera St. Clair (Norway), Andy Sumner (UK)
Review Editors:
Susan Cutter (USA), Etienne Piguet (Switzerland)
Volunteer Chapter Scientist:
Anna Kaijser (Sweden)
This chapter should be cited as:
Olsson
, L., M. Opondo, P. Tschakert, A. Agrawal, S.H. Eriksen, S. Ma, L.N. Perch, and S.A. Zakieldeen, 2014:
Livelihoods and poverty. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and
Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir,
M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken,
P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and
New York, NY, USA, pp. 793-832.
13
794
Executive Summary ........................................................................................................................................................... 796
13.1. Scope, Delineations, and Definitions: Livelihoods, Poverty, and Inequality ........................................................... 798
13.1.1. Livelihoods ....................................................................................................................................................................................... 798
13.1.1.1. Dynamic Livelihoods and Trajectories ................................................................................................................................ 798
13.1.1.2. Multiple Stressors .............................................................................................................................................................. 798
13.1.2. Dimensions of Poverty ...................................................................................................................................................................... 799
13.1.2.1. Framing and Measuring Multidimensional Poverty ........................................................................................................... 800
13.1.2.2. Geographic Distribution and Trends of the World’s Poor ................................................................................................... 801
13.1.2.3. Spatial and Temporal Scales of Poverty ............................................................................................................................. 801
13.1.3. Inequality and Marginalization ......................................................................................................................................................... 802
13.1.4. Interactions between Livelihoods, Poverty, Inequality, and Climate Change ..................................................................................... 802
13.2. Assessment of Climate Change Impacts on Livelihoods and Poverty .................................................................... 803
13.2.1. Evidence of Observed Climate Change Impacts on Livelihoods and Poverty .................................................................................... 803
13.2.1.1. Impacts on Livelihood Assets and Human Capabilities ...................................................................................................... 803
13.2.1.2. Impacts on Livelihood Dynamics and Trajectories .............................................................................................................. 805
13.2.1.3. Impacts on Poverty Dynamics: Transient and Chronic Poverty ........................................................................................... 805
13.2.1.4. Poverty Traps and Critical Thresholds ................................................................................................................................. 806
13.2.1.5. Multidimensional Inequality and Vulnerability .................................................................................................................. 807
Box 13-1. Climate and Gender Inequality: Complex and Intersecting Power Relations .................................................. 808
13.2.2. Understanding Future Impacts of and Risks from Climate Change on Livelihoods and Poverty ........................................................ 810
13.2.2.1. Projected Risks and Impacts by Geographic Region .......................................................................................................... 810
13.2.2.2. Anticipated Impacts on Economic Growth and Agricultural Productivity ........................................................................... 810
13.2.2.3. Implications for Livelihood Assets, Trajectories, and Poverty Dynamics .............................................................................. 812
13.2.2.4. Impacts on Transient and Chronic Poverty, Poverty Traps, and Thresholds ......................................................................... 812
13.3. Assessment of Impacts of Climate Change Responses on Livelihoods and Poverty .............................................. 813
13.3.1. Impacts of Mitigation Responses ...................................................................................................................................................... 813
13.3.1.1. The Clean Development Mechanism ................................................................................................................................. 813
13.3.1.2. Reduction of Emissions from Deforestation and Forest Degradation ................................................................................. 814
13.3.1.3. Voluntary Carbon Offsets .................................................................................................................................................. 814
13.3.1.4. Biofuel Production and Large-Scale Land Acquisitions ...................................................................................................... 814
13.3.2. Impacts of Adaptation Responses on Poverty and Livelihoods ......................................................................................................... 815
13.3.2.1. Impacts of Adaptation Responses on Livelihoods and Poverty .......................................................................................... 815
13.3.2.2. Insurance Mechanisms for Adaptation .............................................................................................................................. 816
Table of Contents
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13
13.4. Implications of Climate Change for Poverty Alleviation Efforts ............................................................................. 816
13.4.1. Lessons from Climate-Development Efforts ...................................................................................................................................... 816
Box 13-2. Lessons from Social Protection, Disaster Risk Reduction, and Energy Access ............................................................ 817
13.4.2. Toward Climate-Resilient Development Pathways ............................................................................................................................ 818
13.5. Synthesis and Research Gaps ................................................................................................................................. 818
References ......................................................................................................................................................................... 819
Frequently Asked Questions
13.1: What are multiple stressors and how do they intersect with inequalities to influence livelihood trajectories? ................................ 799
13.2: How important are climate change-driven impacts on poverty compared to other drivers of poverty? ............................................ 802
13.3: Are there unintended negative consequences of climate change policies for people who are poor? ............................................... 813
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Chapter 13 Livelihoods and Poverty
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Executive Summary
This chapter discusses how livelihoods, poverty and the lives of poor people, and inequality interact with climate change, climate variability,
and extreme events in multifaceted and cross-scalar ways. It examines how current impacts of climate change, projected impacts up until
2100, and responses to climate change affect livelihoods and poverty. The Fourth Assessment Report stated that socially and economically
disadvantaged and marginalized people are disproportionally affected by climate change. However, no comprehensive review of climate
change, poverty, and livelihoods has been undertaken to date by the IPCC. This chapter addresses this gap, presenting evidence of the dynamic
interactions between these three principal factors. At the same time, the chapter recognizes that climate change is rarely the only factor that
affects livelihood trajectories and poverty dynamics; climate change interacts with a multitude of non-climatic factors, which makes detection
and attribution challenging.
Climate-related hazards exacerbate other stressors, often with negative outcomes for livelihoods, especially for people living in
poverty (high confidence).
• Climate-related hazards, including subtle shifts and trends to extreme events, affect poor people’s lives directly through impacts on
livelihoods, such as losses in crop yields, destroyed homes, food insecurity, and loss of sense of place, and indirectly through increased food
prices (robust evidence, high agreement). {13.2.1, 13.3}
• Changing climate trends lead to shifts in rural livelihoods with mixed outcomes, such as from crop-based to hybrid livestock-based
livelihoods or to wage labor in urban employment. Climate change is one stressor that shapes dynamic and differential livelihood
trajectories (robust evidence, high agreement). {13.1.4, 13.2.1.2}
• Urban and rural transient poor who face multiple deprivations slide into chronic poverty as a result of extreme events, or a series of events,
when unable to rebuild their eroded assets. Poverty traps also arise from food price increase, restricted mobility, and discrimination (limited
evidence, high agreement). {13.2.1.3-4}
• Many events that affect poor people are weather-related and remain unrecognized by standard climate observations in many low-income
countries, owing to short time series and geographically sparse, aggregated, or partial data, inhibiting detection and attribution. Such
events include short periods of extreme temperature, minor changes in the distribution of rainfall, and strong wind events (robust evidence,
high agreement). {13.2.1}
Observed evidence suggests that climate change and climate variability worsen existing poverty, exacerbate inequalities, and
trigger both new vulnerabilities and some opportunities for individuals and communities. Poor people are poor for different
reasons and thus are not all equally affected, and not all vulnerable people are poor. Climate change interacts with non-climatic
stressors and entrenched structural inequalities to shape vulnerabilities (very high confidence, based on robust evidence, high
agreement).
• Socially and geographically disadvantaged people exposed to persistent inequalities at the intersection of various dimensions of
discrimination based on gender, age, race, class, caste, indigeneity, and (dis)ability are particularly negatively affected by climate change
and climate-related hazards. Context-specific conditions of marginalization shape multidimensional vulnerability and differential impacts.
{13.1.2.3, 13.1.3., 13.2.1.5}
• Existing gender inequalities are increased or heightened by climate-related hazards. Gendered impacts result from customary and new
roles in society, often entailing higher workloads, occupational hazards indoors and outdoors, psychological and emotional distress, and
mortality in climate-related disasters. {13.2.1.5}
• There is little evidence that shows positive impacts of climate change on poor people, except isolated cases of social asset accumulation,
agricultural diversification, disaster preparedness, and collective action. The more affluent often take advantage of shocks and crises, given
their flexible assets and power status. {13.1.4, 13.2.1.4; Figure 13-3}
Climate change will create new poor between now and 2100, in developing and developed countries, and jeopardize sustainable
development. The majority of severe impacts are projected for urban areas and some rural regions in sub-Saharan Africa and
Southeast Asia (medium confidence, based on medium evidence, medium agreement).
• Future impacts of climate change, extending from the near term to the long term, mostly expecting 2°C scenarios, will slow down
economic growth and poverty reduction, further erode food security, and trigger new poverty traps, the latter particularly in urban areas
and emerging hotspots of hunger. {13.2.2.2, 13.2.2.4, 13.4}
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Livelihoods and Poverty Chapter 13
• Climate change will exacerbate multidimensional poverty in most developing countries, including high mountain states, countries at risk
from sea level rise, and countries with indigenous peoples. Climate change will also create new poverty pockets in countries with increasing
inequality, in both developed and developing countries. {13.2.2}
• Wage-labor dependent poor households that are net buyers of food will be particularly affected due to food price increases, in urban and
rural areas, especially in regions with high food insecurity and high inequality (particularly in Africa), although the agricultural self-employed
could benefit {13.2.2.3-4}
Current policy responses for climate change mitigation or adaptation will result in mixed, and in some cases even detrimental,
outcomes for poor and marginalized people, despite numerous potential synergies between climate policies and poverty reduction
(medium confidence, based on limited evidence, high agreement).
• Mitigation policies with social co-benefits expected in their design, such as Clean Development Mechanism (CDM) and Reduction of
Emissions from Deforestation and Forest Degradation (REDD+), have had limited or no effect in terms of poverty alleviation and
sustainable development. {13.3.1.1-2}
• Mitigation efforts focused on land acquisition for biofuel production show preliminary negative impacts on the lives of poor people, such
as dispossession of farmland and forests, in many developing countries, particularly for indigenous peoples and (women) smallholders.
{13.3.1.4}
• Insurance schemes, social protection programs, and disaster risk reduction may enhance long-term livelihood resilience among poor and
marginalized people, if policies address multidimensional poverty. {13.3.2.2, 13.4.1}
• Climate-resilient development pathways will have only marginal effects on poverty reduction, unless structural inequalities are addressed
and needs for equity among poor and non-poor people are met. {13.4.2}
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Chapter 13 Livelihoods and Poverty
13
13.1. Scope, Delineations, and Definitions:
Livelihoods, Poverty, and Inequality
Understanding the impacts of climate change on livelihoods and
p
overty requires examining the complexities of poverty and the lives of
poor and non-poor people, as well as the multifaceted and cross-scalar
intersections of poverty and livelihoods with climate change. This chapter
is devoted to exploring poverty in relation to climate change, a novelty
in the IPCC. It uses a livelihood lens to assess the interactions between
climate change and multiple dimensions of poverty. We use the term
“the poor,” not to homogenize, but to describe people living in poverty,
people facing multiple deprivations, and the socially and economically
disadvantaged, as part of a conceptualization broader than income-
based measures of poverty, acknowledging gradients of prosperity and
poverty. This livelihood lens also reveals how inequalities perpetuate
poverty to shape differential vulnerabilities and in turn the differentiated
impacts of climate change on individuals and societies. The chapter first
presents the concepts of livelihoods, poverty, and inequality, and their
relationships to each other and to climate change. Second, it describes
observed impacts of weather events and climate on livelihoods and
rural and urban poor people as well as projected impacts up to 2100.
We use “weather events and climate” as an umbrella term for climate
change, climate variability, and extreme events, and also highlight subtle
shifts in precipitation and localized weather events. Third, this chapter
discusses impacts of climate change mitigation and adaptation responses
on livelihoods and poverty. Finally, it outlines implications for poverty
alleviation efforts and climate-resilient development pathways.
Livelihoods and Poverty is a new chapter in the AR5. Although the WGII
AR4 contributions mentioned poverty, as one of several non-climatic
factors contributing to vulnerability, as a serious obstacle to effective
adaptation, and in the context of endemic poverty in Africa (Chapters
7, 8, 18, 20), no systematic assessment was undertaken. Livelihoods were
more frequently addressed in the AR4 and in the Special Report on
Managing the Risks of Extreme Events and Disasters to Advance Climate
Change Adaptation (SREX), predominantly with reference to livelihood
strategies and opportunities, diversification, resource-dependent
communities, and sustainability. Yet, a comprehensive livelihood lens
for assessing impacts was lacking. This chapter addresses these gaps.
It assesses how climate change intersects with other stressors to shape
livelihood choices and trajectories, to affect the spatial and temporal
dimensions of poverty dynamics, and to reduce or exacerbate inequalities
given differential vulnerabilities.
13.1.1. Livelihoods
Livelihoods (see also Glossary) are understood as the ensemble or
opportunity set of capabilities, assets, and activities that are required
to make a living (Chambers and Conway, 1992; Ellis et al., 2003). They
depend on access to natural, human, physical, financial, social, and
cultural capital (assets); the social relations people draw on to combine,
transform, and expand their assets; and the ways people deploy and
enhance their capabilities to act and make lives meaningful (Scoones,
1998; Bebbington, 1999). Livelihoods are dynamic and people adapt and
change their livelihoods with internal and external stressors. Ultimately,
successful livelihoods transform assets into income, dignity, and agency,
t
o improve living conditions, a prerequisite for poverty alleviation (Sen,
1981).
Livelihoods are universal. Poor and rich people both pursue livelihoods
to make a living. However, as shown in this chapter, the adverse impacts
of weather events and climate increasingly threaten and erode basic needs,
capabilities, and rights, particularly among poor and disenfranchised
people, in turn reshaping their livelihoods (UNDP, 2007; Leary et al.,
2008; Adger, 2010; Quinn et al., 2011). Some livelihoods are directly
climate sensitive, such as rainfed smallholder agriculture, seasonal
employment in agriculture (e.g., tea, coffee, sugar), fishing, pastoralism,
and tourism. Climate change also affects households dependent on
informal livelihoods or wage labor in poor urban settlements, directly
through unsafe settlement structures or indirectly through rises in food
prices or migration.
13.1.1.1. Dynamic Livelihoods and Trajectories
A livelihood lens is a grounded and multidimensional perspective that
recognizes the flexibility and constraints with which people construct
their complex lives and adapt their livelihoods in dynamic ways. By
paying attention to the wider institutional, cultural, and policy contexts
as well as shocks, seasonality, and trends, this lens reveals processes
that push people onto undesirable trajectories or toward enhanced well-
being. Better infrastructure and technology as well as diversification of
assets, activities, and social support capabilities can boost livelihoods,
spreading risks and broadening opportunities (Batterbury, 2001; Ellis
et al., 2003; Clot and Carter, 2009; Carr, 2013; Reed et al., 2013). The
sustainable livelihoods framework (Chambers and Conway, 1992) is
widely used for identifying how specific strategies may lead to cycles
of livelihood improvements or critical thresholds beyond which certain
livelihoods are no longer sustainable (Sabates-Wheeler et al., 2008). It
emerged as a reaction to the predominantly structural views of poverty
and “underdevelopment” in the 1970s and became adopted by many
researchers and development agencies (Ellis and Biggs, 2001). With
the neoliberal turn in the late 1980s, the livelihoods approach became
associated with a more individualistic development agenda, stressing
various forms of capital (Scoones, 2009). Consequently, it has been
criticized for its analytical limitations, such as measuring capitals or
assets, especially social capital, and for not sufficiently explaining wider
structural processes (e.g., policies) and ecological impacts of livelihood
decisions (Small, 2007; Scoones, 2009). An overemphasis on capitals
also eclipses power dynamics and the position of households in class,
race, and other dimensions of inequality (Van Dijk, 2011).
13.1.1.2. Multiple Stressors
Livelihoods rarely face only one stressor or shock at a time. The literature
emphasizes the synergistic relationship between weather events and
climate and a variety of other environmental, social, economic, and
political stressors; together, they impinge on livelihoods and reinforce
each other in the process, often negatively (Reid and Vogel, 2006;
Schipper and Pelling, 2006; Easterling et al., 2007; IPCC, 2007; Morton,
2007; Tschakert, 2007; O’Brien et al., 2008; Eriksen and Silva, 2009;
Eakin and Wehbe, 2009; Ziervogel et al., 2010). “Double losers” may
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Livelihoods and Poverty Chapter 13
13
emerge from simultaneous exposure to climatic change and other
stressors such as the spread of infectious diseases, rapid urbanization,
and economic globalization, where climate change acts as a threat
multiplier, further marginalizing vulnerable groups (O’Brien and Leichenko,
2000; Eriksen and Silva, 2009). Climatic and other stressors affect
livelihoods at different scales: spatial (e.g., village, nation) or temporal
(e.g., annual, multi-annual). Both direct and indirect impacts are often
amplified or weakened at different levels. Global or regional processes
generate a variety of stressors, typically mediated by cross-level
institutions, that result in locally experienced shocks (Reid and Vogel,
2006; Thomas et al., 2007; Paavola, 2008; Pouliotte et al., 2009; see also
Figure 13-1 in FAQ 13.1).
Multiple stressors, simultaneous and in sequence, shape livelihood
dynamics in distinct ways due to inequalities and differential vulnerabilities
between and within households. More affluent households may be able
to capitalize on shocks and crises while poorer households with fewer
options are forced to erode their assets. Limited ability to adapt and
some coping strategies may result in adverse consequences. Such
maladaptive actions (see Glossary, and Chapters 14, 16) undermine the
long-term sustainability of livelihoods, resulting in downward trajectories,
poverty traps, and exacerbated inequalities (Ziervogel et al., 2006;
Tanner and Mitchell, 2008; Barnett and O’Neill, 2010).
13.1.2. Dimensions of Poverty
Poverty is a complex concept with conflicting definitions and considerable
disagreement in terms of framings, methodologies, and measurements.
Despite different approaches emphasizing distinct aspects of poverty
at the individual or collective level—such as income, capabilities, and
quality of life (Laderchi et al., 2003)—poverty is recognized as
multidimensional (UNDP, 1990). It is influenced by social, economic,
institutional, political, and cultural drivers; its reversal requires efforts
Frequently Asked Questions
FAQ 13.1 | What are multiple stressors and how do they intersect with inequalities
to influence livelihood trajectories?
Multiple stressors are simultaneous or subsequent
conditions or events that provoke/require changes
in livelihoods. Stressors include climatic (e.g., shifts
in seasons), socioeconomic (e.g., market volatility),
and environmental (e.g., destruction of forest) factors,
that interact and reinforce each other across space
and time to affect livelihood opportunities and
decision making (see Figure 13-1). Stressors that
originate at the macro level include climate change,
globalization, and technological change. At the
regional, national, and local levels, institutional
context and policies shape possibilities and pitfalls
for lessening the effects of these stressors. Which
specific stressors ultimately result in shocks for
particular livelihoods and households is often
mediated by institutions that connect the local level
to higher levels. Moreover, inequalities in low-,
medium-, and high-income countries often amplify
the effects of these stressors. This is particularly the
case for livelihoods and households that have
limited asset flexibility and/or those that experience
disadvantages and marginalization due to gender,
age, class, race, (dis)ability, or being part of a particular
indigenous or ethnic group. Weather events and
climate compound these stressors, allowing some to benefit and enhance their well-being while others experience
severe shocks and may slide into chronic poverty. Who is affected, how, where, and for how long depends on local
contexts. For example, in the Humla district in Nepal, gender roles and caste relations influence livelihood trajectories
in the face of multiple stressors including shifts in the monsoon season (climatic), limited road linkages (socioeconomic),
and high elevation (environmental). Women from low castes have adapted their livelihoods by seeking more day-
labor employment, whereas men from low castes ventured into trading on the Nepal-China border, previously an
exclusively upper caste livelihood.
Livelihoods
Institutions such as:
•
Social protection
• Relief organizations
• Disaster prevention
Displacement
Destroyed
homes
Food
crisis
Figure 13-1 | Multiple stressors related to climate change, globalizations, and
technological change interact with national and regional institutions to create shocks
to place-based livelihoods, inspired by Reason (2000).
Climate change
Globalizations
Technological change
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Chapter 13 Livelihoods and Poverty
13
i
n multiple domains that promote opportunities and empowerment, and
enhance security (World Bank, 2001). In addition to material deprivation,
multidimensional conceptions of poverty consider a sense of belonging
and socio-cultural heritage (O’Brien and Leichenko, 2003), identity, and
agency, or “the culturally constrained capacity to act” (Ahearn, 2001,
p. 54). The AR4 identified poverty as “the most serious obstacle to
effective adaptation” (Confalonieri et al., 2007, p. 417).
13.1.2.1. Framing and Measuring Multidimensional Poverty
Over the last 6 decades, conceptualizations of poverty have broadened,
expanding the basis for understanding poverty and its drivers. Poverty
measurements now better capture multidimensional characteristics
and spatial and temporal nuances. Attention to multidimensional
deprivations—such as hunger; illiteracy; unclean drinking water; lack
of access to health, credit, or legal services; social exclusion; and
disempowerment—have shifted the analytical lens to the dynamics of
poverty and its institutionalization within social and political norms
(UNDP, 1994; Sen, 1999; World Bank, 2001). Regardless of these shifting
conceptualizations over time, comparable and reliable measures remain
challenging and income per capita remains the default method to
account for the depth of global poverty.
In climate change literature, poverty and poverty reduction have been
predominantly defined through an economic lens, reflecting various
growth and development discourses (Sachs, 2006; Collier, 2007). Less
a
ttention has been paid to relational poverty, produced through
material social relations and in relation to privilege and wealth (Sen,
1976; Mosse, 2010; Alkire and Foster, 2011; UNDP, 2011a). Yet, such
framing allows for addressing the social and political contexts that
generate and perpetuate poverty and structural vulnerability to climate
change (McCright and Dunlap, 2000; Bandiera et al., 2005; Leichenko
and O’Brien, 2008). Many climate policies to date favor market-based
responses using sector-specific and economic growth models of
development, although some responses may slow down achievements
of international development such as those outlined in the Millennium
Development Goals (MDGs). For instance, the World Bank encourages
“mitigation, adaptation, and the deployment of technologies” that
“allow[s] developing countries to continue their growth and reduce
poverty” (World Bank, 2010, p. 257), mainly promoted through market
tools. A relational approach to poverty highlights the integral role of
poor people in all social relations (Pogge, 2009; O’Brien et al., 2010;
UNRISD, 2010; Gasper et al., 2013; St.Clair and Lawson, 2013). It
emphasizes equity, human security, and dignity (O’Connor, 2002; Mosse,
2010). Akin to the capabilities approach (Sen, 1985, 1999; Nussbaum,
2001, 2011; Alkire, 2005), the relational approach stresses the needs,
skills, and aims of poor people while tackling structural causes of
poverty, inequalities, and uneven power relations.
The IPCC AR4 (Yohe et al., 2007) highlighted that—with very high
confidence—climate change will impede the ability of nations to
alleviate poverty and achieve sustainable development, as measured
by progress toward the MDGs. Empirical assessments of the impact of
0.00 – 0.06
0.06 – 0.11
0.11 – 0.18
0.18 – 0.30
0.30 – 0.47
no data
0.01 – 0.20
0.20 – 0.26
0.26 – 0.31
0.31 – 0.35
0.35 – 0.68
no data
10080604020
0
100
80
60
40
20
Philippines
Nicaragua
Gabon
Colombia
Thailand
Vietnam
Vietnam
Cameroon
Kenya
India
Gambia
Senegal
Uganda
Benin
Guinea
Niger
Central African
Republic
Rwanda
Malawi
Malawi
Madagascar
Poverty Gap Index
Poverty Gap Index
Uzbekistan
Laos
Haiti
Population below the international poverty line $1.25 per day (%)
Population in multidimensional poverty (%)
(a) (b)
Figure 13-2 | (a) Multidimensional poverty and income-based poverty using the International Poverty Line $1.25 per day (in Purchasing Power Parity terms), with linear regression relationship
(dotted line) based on 96 countries (UNDP, 2011b). The position of the countries relative to the dotted line illustrates the extent to which these two poverty measures are similar or divergent.
(b) The map insets show the intensity of poverty in two countries, based on the Poverty Gap Index at district level (per capita measure of the shortfall in welfare of the poor from the poverty
line, expressed as a ratio of the poverty line): the darker the shading, the larger the shortfall.
801
Livelihoods and Poverty Chapter 13
13
c
limate change on MDG attainment are limited (Fankhauser and
Schmidt-Traub, 2011), and the failure to reach these goals by 2015 has
significant non-climatic causes (e.g., Hellmuth et al., 2007; UNDP, 2007).
The 2010 UNDP Multidimensional Poverty Index, measuring intensity of
poverty based on patterns of simultaneous deprivations in basic services
(education, health, and standard of living) and core human functionings,
states that close to 1.7 billion people face multidimensional poverty, a
significantly higher number than the 1.2 billion (World Bank, 2012a)
indicated by the International Poverty Line (IPL) set at $1.25 per day.
Figure 13-2 depicts country-level examples of how the two poverty
measures differ.
Caution is required for poverty projections. Estimates of poverty made
using national accounts means (see Chapter 19) yield drastically
different estimates to those produced by survey means, both for
current estimates and future projections (Edward and Sumner, 2013a).
Diverse conceptions of poverty further complicate projections, as
multidimensional conceptions rely on concepts difficult to measure
and compare. Data availability constrains current estimates let alone
projections and their core assumptions (Alkire and Santos, 2010; Karver
et al., 2012).
13.1.2.2. Geographic Distribution and Trends of the World’s Poor
Geographic patterns of poverty are uneven and shifting. Despite its
limitations, most comparisons to date rely on the IPL. In the remainder
of the text, we use the World Bank income-based poverty categories
for countries (low-income countries, lower-middle-income countries,
upper-middle-income countries, and high-income countries); these
categories are more precise and more accurate for describing climate
change impacts on poverty than the terminology adopted in the
Summary for Policymakers and the respective chapter Executive
Summaries (i.e., ‘developing’ and ‘developed’ countries). Moreover,
much of the assessed literature is based on these categories. In 1990,
most of the world’s $1.25 and $2 poor lived in low-income countries
(LICs). By 2008, the majority of the poor living on $1.25 and $2 (>70%)
resided in lower- and upper-middle-income countries (LMICs and UMICs),
in part because some populous LICs such as India, Nigeria, and Pakistan
grew in per capita income to MIC status (Sumner, 2010, 2012a). Estimates
suggest about 1 billion people currently living on less than $1.25 per
day in MICs and a second billion between $1.25 and $2, with an
additional 320 million and 170 million in LICs, respectively (Sumner,
2012b). About 70% of the poor subsisting on $1.25 per day live in rural
areas in the global South (IFAD, 2011), despite worldwide urbanization.
Yet, this poverty line understates urban poverty as it does not fully
account for the higher costs of food and non-food items in many urban
contexts (Mitlin and Satterthwaite, 2013). Of the approximately 2.4
billion living on less than $2 per day, half live in India and China. At the
same time, relative poverty is rising in HICs. Many European countries
face rapid increases in poverty, unemployment, and the number of
working poor due to recent austerity measures. For example, 20% of
Spanish citizens were ranked poor in 2009 (Ortiz and Cummins, 2013).
See also Chapter 23.
The shift in distribution of global poverty toward MICs and the increase
in relative poverty in HICs challenge the orthodox view that most of the
w
orld’s poorest people live in the poorest countries, and suggests that
substantial pockets of poverty persist in countries with higher levels of
average per capita income. Understanding this shift in the geography
of poverty and available social safety nets is vital for assessing climate
change impacts on poverty. To date, both climate finance and research
on climate impacts and vulnerabilities are directed largely toward LICs.
Less attention has been paid to poor people in MICs and HICs. In the
upper and lower MICs, the incidence of $2 per day poverty, despite
declines, remains as high as 60% and 20%, respectively (Sumner, 2012b).
Projections for 2030 suggest $2 per day poverty as high as 963 million
people in sub-Saharan Africa and 851 million in India (Sumner et al.,
2012; Edward and Sumner, 2013a). However, uncertainty is high in
terms of future growth and inequality trends; by 2030, $1.25 and $2
per day global poverty could be reduced to 300 million and 600 million
respectively or remain at or above current levels, including in stable
MICs (Edward and Sumner, 2013a). These future scenarios become more
uncertain if climate change impacts on people who are socially and
economically disadvantaged are taken into account or diversion of
resources from poverty reduction and social protection to mitigation
strategies is considered.
13.1.2.3. Spatial and Temporal Scales of Poverty
Poverty is also socially distributed, across spatial and temporal scales.
Not everybody is poor in the same way. Spatially, factors such as access
to and control over resources and institutional linkages from individuals
to the international level affect poverty distribution (Anderson and
Broch-Due, 2000; Murray, 2002; O’Laughlin, 2002; Rodima-Taylor, 2011).
Even at the household level, poverty differs between men and women
and age groups, yet data constraints impede systematic intra-household
analysis (Alkire and Santos, 2010). The distribution of poverty also varies
temporally, typically between chronic and transient poverty (Sen, 1981,
1999). Chronic poverty describes an individual deprivation, per capita
income, or consumption levels below the poverty line over many years
(Gaiha and Deolalikar, 1993; Jalan and Ravallion, 2000; Hulme and
Shepherd, 2003). Transient poverty denotes a temporary state of
deprivation, and is frequently seasonal and triggered by an individual’s
or household’s inability to maintain income or consumption levels in
times of shocks or crises (Jalan and Ravallion, 1998).
Individuals and households can fluctuate between different degrees of
poverty and shift in and out of deprivation, vulnerability, and well-being
(Leach et al., 1999; Little et al., 2008; Sallu et al., 2010). Yet, the most
disadvantaged often find themselves in poverty traps, or situations in
which escaping poverty becomes impossible without external assistance
due to unproductive or inflexible asset portfolios (Barrett and McPeak,
2006). A poverty trap can also be seen as a “critical minimum asset
threshold, below which families are unable to successfully educate their
children, build up their productive assets, and move ahead economically
over time” (Carter et al., 2007 p. 837). As of 2008, a total of 320 to 443
million of people were trapped in chronic poverty (Chronic Poverty
Research Centre, 2008), leading Sachs (2006) to label less than $1.25
per day poverty as a trap in itself. Poverty traps at the national level
are often related to poor governance, reduced foreign investment, and
conflict (see Chapters 10, 12).
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13.1.3. Inequality and Marginalization
Specific livelihoods and poverty alone do not necessarily make people
vulnerable to weather events and climate. The socially and economically
disadvantaged and the marginalized are disproportionately affected by
the impacts of climate change and extreme events (robust evidence;
Kates, 2000; Paavola and Adger, 2006; Adger et al., 2007; Cordona et
al., 2012). The AR4 identified poor and indigenous peoples in North
America (Field et al., 2007) and in Africa (Boko et al., 2007) as highly
vulnerable. Vulnerability, or the propensity or predisposition to be
adversely affected (IPCC, 2012a) by climatic risks and other stressors (see
also Glossary), emerges from the intersection of different inequalities, and
uneven power structures, and hence is socially differentiated (Sen, 1999;
Banik, 2009; IPCC, 2012a). Vulnerability is often high among indigenous
peoples, women, children, the elderly, and disabled people who experience
multiple deprivations that inhibit them from managing daily risks and
shocks (Eriksen and O’Brien, 2007; Ayers and Huq, 2009; Boyd and
Juhola, 2009; Barnett and O’Neill, 2010; O’Brien et al., 2010; Petheram
et al., 2010) and may present significant barriers to adaptation.
Global income inequality has been relatively consistent since the late
1980s. In 2007, the top quintile of the world’s population received 83%
of the total income whereas the bottom quintile took in 1% (Ortiz and
Cummins, 2011). Since 2005, between-country inequality has been
falling more quickly and, consequently, has triggered a notable decline
in total global inequality in the last few years (Edward and Sumner,
2013b). However, within-country inequality is rising in Asia, especially
China, albeit from relatively low levels, and is falling in Latin America,
albeit from very high levels, while trends in sub-Saharan Africa are
difficult to discern regionally (Ravallion and Chen, 2012). Income
inequality is rising in many fast growing LICs and MICs (Dollar et al.,
2013; Edward and Sumner, 2013b). It is also growing in many HICs owing
to a combination of factors such as changing tax systems, privatization
of social services, labor market regulations, and technological change
(
OECD, 2011). The 2008 financial crisis, combined with climate change,
has further threatened economic growth in HICs, such as the UK, and
resources available for social policies and welfare systems (Gough,
2010). Recognizing how inequality and marginalization perpetuate
poverty is a prerequisite for climate-resilient development pathways
(see Section 13.4; Chapters 1, 20, 27).
13.1.4. Interactions between Livelihoods, Poverty,
Inequality, and Climate Change
This chapter opens its analytical lens from a conventional focus on
the poor in LICs as the prime victims of climate change to a broader
understanding of livelihood and poverty dynamics and inequalities,
revealing the highly unequal impacts of climate change. It highlights
the complex relationship between climate change and poverty. The
SREX recognizes that addressing structural inequalities that create and
sustain poverty and vulnerability (Huq et al., 2005; Schipper, 2007;
Lemos et al., 2007; Boyd and Juhola, 2009; Williams, 2010; Perch, 2011)
is a crucial precondition for confronting climate change (IPCC, 2012a).
If ignored, uneven social relations that disproportionally burden poor
people with climate change’s negative impacts provoke maladaptation
(Barnett and O’Neill, 2010).
Poverty and persistent inequality are the “most salient of the conditions
that shape climate-related vulnerability” (Ribot, 2010, p. 50). They affect
livelihood options and trajectories, and create conditions in which people
have few assets to liquidate in times of hardship or crisis (Mearns and
Norton, 2010). People who are poor and marginalized usually have the
least buffer to face even modest climate hazards and suffer most from
successive events with little time for recovery. They are the first to
experience asset erosion, poverty traps, and barriers and limits to
adaptation. As shown in Sections 13.2 and 13.3, climate change is an
additional burden to people in poverty (very high confidence), and it
Frequently Asked Questions
FAQ 13.2 | How important are climate change-driven impacts on poverty
compared to other drivers of poverty?
Climate change-driven impacts are one of many important causes of poverty. They often act as a threat multiplier,
meaning that the impacts of climate change compound other drivers of poverty. Poverty is a complex social and
political problem, intertwined with processes of socioeconomic, cultural, institutional, and political marginalization,
inequality, and deprivation, in low-, middle-, and even high-income countries. Climate change intersects with many
causes and aspects of poverty to worsen not only income poverty but also undermine well-being, agency, and a
sense of belonging. This complexity makes detecting and measuring attribution to climate change exceedingly
difficult. Even modest changes in seasonality of rainfall, temperature, and wind patterns can push transient poor
and marginalized people into chronic poverty as they lack access to credit, climate forecasts, insurance, government
support, and effective response options, such as diversifying their assets. Such shifts have been observed among
climate-sensitive livelihoods in high mountain environments, drylands, and the Arctic, and in informal settlements
and urban slums. Extreme events, such as floods, droughts, and heat waves, especially when occurring in a series,
can significantly erode poor people’s assets and further undermine their livelihoods in terms of labor productivity,
housing, infrastructure, and social networks. Indirect impacts, such as increases in food prices due to climate-related
disasters and/or policies, can also harm both rural and urban poor people who are net buyers of food.
803
Livelihoods and Poverty Chapter 13
13
w
ill force poor people from transient into chronic poverty and create
new poor (medium confidence).
The complex interactions among weather events and climate, dynamic
livelihoods, multidimensional poverty and deprivation, and persistent
inequalities, including gender inequalities, create an ever-shifting context
of risk. The SREX concluded that climate change, climate variability, and
extreme events synergistically add on to and often reinforce other
environmental, social, and political calamities (IPCC, 2012a). Despite
the recognition of these complex interactions, the literature shows no
single conceptual framework that captures them concurrently, and few
studies exist that overlay gradual climatic shifts or rapid-onset events
onto livelihood risks. Hence, explicit attention to how livelihood dynamics
interact with climatic and non-climatic stressors is useful for identifying
processes that push poor and vulnerable people onto undesirable
trajectories, trap them in destitution, or facilitate pathways toward
enhanced well-being. Figure 13-3 illustrates these dynamics as well as
critical thresholds in livelihood trajectories.
13.2. Assessment of Climate Change Impacts
on Livelihoods and Poverty
This section reviews the evidence and agreement about the relationships
among climate change, livelihoods, poverty, and inequality. Building on
deductive reasoning and theorized linkages about these dynamic
relationships, this section draws on a wide range of empirical case
studies and simulations to illustrate linkages across multiple scales,
contexts, and social and environmental processes and to assess impacts
of climate change. Although cases of observed impacts often rely on
qualitative data and at times lack methodological clarity in terms of
detection and attribution, they provide a vital evidence base for
conveying these complex relationships. This section first describes
observed impacts to date (Section 13.2.1) and then projected risks and
impacts (Section 13.2.2).
13.2.1. Evidence of Observed Climate Change Impacts
on Livelihoods and Poverty
Weather events and climate affect the lives and livelihoods of millions
of poor people (IPCC, 2012b). Even minor changes in precipitation
amount or temporal distribution, short periods of extreme temperatures,
or localized strong winds can harm livelihoods (Douglas et al., 2008;
Ostfeld, 2009; Midgley and Thuiller, 2011; Bele et al., 2013; Bryan et al.,
2013). Many such events remain unrecognized given that standard
climate observations typically report precipitation or temperature by
month, season, or year, thus obscuring changes that shape decision
making, for instance, in agriculture (Tennant and Hewitson, 2002;
Barron et al., 2003; Usman and Reason, 2004; Douglas et al., 2008;
Lacombe et al., 2012; Salack et al., 2012). This difficulty in detection and
attribution is compounded by a lack of long-term continuous and dense
networks of climate data in many LICs (UNECA, 2011). Felt experiences
of events such as drought, as shown among the Sumbanese in Eastern
Indonesia through phenomenological research on perceptions of
climatic phenomena, such as shade and dew (Orr et al., 2012), further
add to the complexity.
13.2.1.1. Impacts on Livelihood Assets and Human Capabilities
Climate change, climate variability, and extreme events interact with
numerous aspects of people’s livelihoods. This section presents empirical
evidence of impacts on natural, physical, financial, human, and social
and cultural assets (see also Chapters 22 to 29). Impacts on access to
assets, albeit important, are poorly documented in the literature, as are
impacts on power relations and active struggles in designing effective
and relational livelihood arrangements.
Weather events and climate affect natural assets on which certain
livelihoods depend directly, such as rivers, lakes, and fish stocks (robust
evidence; Thomas et al., 2007; Nelson and Stathers, 2009; Osbahr et al.,
2010; Bunce et al., 2010a,b; D’Agostino and Sovacool, 2011; see also
Chapters 3, 4, 5, 6, 30). During the 20th century, water temperatures
increased and winds decreased in Lake Tanganyika (Adrian et al., 2009;
Verburg and Hecky, 2009; Tierney et al., 2010). Since the late 1970s, a
drop in primary production and fish catches, a key protein source, has
been observed, and climate change may exceed the effects of overfishing
and other human impacts in this area (O’Reilly et al., 2003). The Middle
East and North Africa (MENA) face dwindling water resources due to
less precipitation and rising temperatures combined with mounting
water demand due to population and economic growth (Tekken and
Kropp, 2012), resulting in rapidly decreasing water availability that, in
2025, could be 30 to 70% less per person (Sowers et al., 2011). In MENA
(Sowers et al., 2011), the Andes and Himalayas (Orlove, 2009), the
Caribbean (Cashman et al., 2010), Australia (Alston, 2011), and in cities
(Satterthwaite, 2011), policy allocation often favors more affluent
consumers, at the expense of less powerful rural and/or poor users.
Weather events and climate also erode farming livelihoods (see Chapters
7, 9), via declining crop yields (Hassan and Nhemachena, 2008; Apata
et al., 2009; Sissoko et al., 2011; Sietz et al., 2012; Li et al., 2013), at times
compounded by increased pathogens, insect attacks, and parasitic weeds
(Stringer et al., 2007; Byg and Salick, 2009), and less availability of and
access to non-timber forest products (Hertel and Rosch, 2010; Nkem et
al., 2012) and medicinal plants and biodiversity (Van Noordwijk, 2010).
For agropastoral and mixed crop-livestock livelihoods, extreme high
temperatures threaten cattle (Hahn, 1997; Thornton et al., 2007; Mader,
2012; Nesamvuni et al., 2012); in Kenya, for instance, people may shift
from dairy to beef cattle and from sheep to goats (Kabubo-Mariara, 2008).
The most extreme form of erosion of natural assets is the complete
disappearance of people’s land on islands and in coastal regions
(McGranahan et al., 2007; Solomon et al., 2009), exacerbating livelihood
risks due to loss of economic and social assets (see Chapters 5, 29; Perch
and Roy, 2010). Densely populated coastal cities with high poverty such
as Alexandria and Port Said in Egypt (El-Raey et al., 1999), Cotonou in
Benin (Dossou and Glehouenou-Dossou, 2007), and Lagos and Port
Harcourt in Nigeria (Abam et al., 2000; Fashae and Onafeso, 2011) are
already affected by floods and at risk of submersion. Resettlements are
planned for the Limpopo River and the Mekong River Delta (de
Sherbinin et al., 2011) and small island states may become uninhabitable
(Burkett, 2011).
Damage to physical assets due to weather events and climate is well
documented for poor urban settlements, often built in risk-prone
804
Chapter 13 Livelihoods and Poverty
13
Convergence of multiple
stressors and shocks
Convergence of multiple
stressors and shocks
Convergence of multiple
stressors and shocks
Non-supportive
policy environment
Institutional &
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Current policy
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Potential policy
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Current policy
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Potential policy
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Intensity of stressors
Catastrophic
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Livelihood condition
Vulnerable Resilient
Intensity of stressors
Catastrophic
Negligible
Livelihood condition
Vulnerable Resilient
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Intensity of stressors
Catastrophic
Negligible
Livelihood condition
Vulnerable Resilient
1
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Convergence of multiple
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4
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Time Time
Time Time
Critical
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Climatic factors
Socioeconomic factors
Environmental factors
Some few households
Several households
Most households
Intensity of stressors
Catastrophic
Negligible
Livelihood condition
Vulnerable Resilient
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threshold
R
ecent past Near future Recent past Near future
Recent past Near futureRecent past Near future
1
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7
3
(a) Botswana’s drylands (Sallu et al., 2010). Over the past 30 years, rural households have
faced droughts, late onset and increased unpredictability of rainfall, and frost 1 , drying of Lake Xau,
and land degradation 2 . Households responded differently to these stressors, given their financial
and physical assets, diversification of and within livelihood activities, family relations, and
institutional and governmental support. Despite weakening of social networks and declining
livestock due to lack of water 3 , distinct livelihood trajectories emerged. “Accumulators” were
often able to benefit from crises, for instance through access to salaried employment 4 or new
hunting quotas 5 , while “dependent” households showed a degenerative trajectory, losing more
and more livelihood assets, and becoming reliant on governmental support after another period of
convergent stressors 6 . “Diversifiers” had trajectories fluctuating between vulnerable and resilient
states 7 .
(b) Coastal Bangladesh (Pouliotte et al., 2010). In the Sunderbans, a combination of
environmental and socioeconomic factors, out of which climatic stressors appear to play only a
minor role, have changed livelihoods: saltwater intrusion 1 due to the construction and poor
management of the Bangladeshi Coastal Embankment Project, the construction of a dam in India,
local water diversions 2 , and sea level rise and storm surges 3 . The convergence of these stressors
caused households to cross a critical threshold from rice and vegetable cultivation to saltwater
shrimp farming 4 . A strong export market and international donor and national government
support facilitated this shift 5 . However, increasing density of shrimp farming then triggered rising
disease levels 6 . Wealth and power started to become more concentrated among a few affluent
families 7 while livelihood options for the poorer households further diminished due to lacking
resources to grow crops in salinated water, the loss of grazing areas and dung from formerly
accessible rice fields 8 , and rising disease levels 6 .
(c) Mountain environments (McDowell and Hess, 2012). Indigenous Aymara farmers in
highland Bolivia face land scarcity, pervasive poverty, climate change, and lack of infrastructure due
in part to racism and institutional marginalization. The retreat of the Mururata glacier causes water
shortages 1 , compounded by the increased water requirements of cash crops on smaller and
smaller “minifundios” and market uncertainties 2 . High temperatures amplify evaporation, and
flash floods coupled with delayed rainfall cause irrigation canals to collapse 3 . The current policy
environment makes it difficult to access loans and obtain land titles 4 , pushing many farmers onto
downward livelihood trajectories 5 while those who can afford it invest in fruit and vegetable trees
at higher altitudes 6 . Sustained access to land, technical assistance, and irrigation infrastructure
would be effective policy responses to enhance well-being 7 .
(d) Urban flooding in Lagos (Adelekan, 2010). Flooding threatens the livelihoods of people
in Lagos, Nigeria, where >70 % live in slums. Increased severity in rainstorms, sea level rise, and
storm surges 1 coupled with the destruction of mangroves and wetlands 2 , disturb people’s jobs
as traders, wharf workers, and artisans, while destroying physical and human assets. Urban
management, infrastructure for water supply, and stormwater drainage have not kept up with
urban growth 3 . Inadequate policy responses, including uncontrolled land reclamation, make these
communities highly vulnerable to flooding 4 . Only some residents can afford sand and broken
sandcrete blocks 5 . Livelihood conditions in these slums are expected to further erode for most
households 6 . Given policy priorities for the construction of high-income residential areas, current
residents fear eviction 7 .
Figure 13-3 | Illustrative representation of four case studies that describe livelihood dynamics under simultaneous climatic, environmental, and socioeconomic stressors, shocks,
and policy responses – leading to differential livelihood trajectories over time. The red boxes indicate specific critical moments when stressors converge, threatening livelihoods
and well-being. Key variables and impacts numbered in the illustrations correspond to the developments described in the captions.
805
Livelihoods and Poverty Chapter 13
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loodplains and hillsides susceptible to erosion and landslides. Impacts
include homes destroyed by flood water and disrupted water and
sanitation services. Flooding has adversely affected large cities in Africa
(Douglas et al., 2008) and Latin America (Hardoy and Pandiella, 2009;
Hardoy et al., 2011), in predominantly dense informal settlements due
to inadequate drainage, and health infrastructure (UNDP, 2011c). Yet,
upper-middle- and high-income households living in flood-prone areas
or high-risk slopes frequently can afford insurance and lobby for
protective policies, in contrast to poor residents (Hardoy and Pandiella,
2009). Loss of physical assets in poor areas after disasters is often
followed by displacement due to loss of property (Douglas et al., 2008).
Increasing flash floods attributed to climate change (Sudmeier-Rieux et
al., 2012) have severely damaged terraces, orchards, roads, and stream
embankments in the Himalayas (Azhar-Hewitt and Hewitt, 2012; Hewitt
and Mehta, 2012).
Erosion of financial assets as a result of climatic stressors include losses
of farm income and jobs (Hassan and Nhemachena, 2008; Iwasaki et al.,
2009; Alderman, 2010; Jabeen et al., 2010; Alston, 2011) and increased
costs of living such as higher expenses for funerals (Gabrielsson et al.,
2012). In South and Central America, more than 600 weather and extreme
events occurred 2000–2013, resulting in 13,500 fatalities, 52.6 million
people affected, and economic losses of US$45.3 billion (www.emdat.be).
Income losses due to weather events mean less money for agricultural
inputs (seeds, equipment), school tuition, uniforms, and books, and
health expenses throughout the year (Thomas et al., 2007). Flooding in
informal settlements in Lagos undermines job opportunities (Adelekan,
2010).
Equally important, albeit frequently overlooked, is the damage to
human assets as a result of weather events and climate, such as food
insecurity, undernourishment, and chronic hunger due to failed crops
(medium evidence) (Patz et al., 2005; Funk et al., 2008; Zambian
Government, 2011; Gentle and Maraseni, 2012) or spikes in food prices
most severely felt among poor urban populations (Ahmed et al., 2009;
Hertel and Rosch, 2010). During the Ethiopian drought (1998–2000)
and Hurricane Mitch in Nicaragua (1998), poorer households tended to
engage in asset smoothing, reducing their consumption to very low
levels to protect their assets, whereas wealthier households sold assets
and smoothed consumption (Carter et al., 2007). In such cases, poor
people further erode nutritional levels and human health while holding
on to their limited assets. Dehydration, heat stroke, and heat exhaustion
from exposure to heat waves undermine people’s ability to carry out
physical work outdoors and indoors (Semenza et al., 1999; Kakota et al.,
2011). Psychological effects from extreme events include sleeplessness,
anxiety and depression (Byg and Salick, 2009; Keshavarz et al., 2013),
loss of sense of place and belonging (Tschakert et al., 2011; Willox et
al., 2012), and suicide (Caldwell et al., 2004; Alston, 2011) (see also
Chapter 11 and Box CC-HS).
Finally, weather events and climate also erode social and cultural assets.
In some contexts, climatic and non-climatic stressors and changing
trends disrupt informal social networks of the poorest, elderly, women,
and women-headed households, preventing mobilization of labor and
reciprocal gifts (Osbahr et al., 2008; Buechler, 2009) as well as formal
social networks, including social assistance programs (Douglas et al.,
2008). Indigenous peoples (see Chapter 12) witness their cultural points
o
f reference disappearing (Ford, 2009; Bell et al., 2010; Green et al.,
2010).
13.2.1.2. Impacts on Livelihood Dynamics and Trajectories
Weather events and climate also affect livelihood trajectories and
dynamics in livelihood decision making, often in conjunction with cross-
scalar socioeconomic, institutional, or political stressors. Shifting in and
out of hardship and well-being on a seasonal basis is not uncommon.
To a large extent, the shifts from coping and hardship to recovery are
driven by annual and interannual climate variability, but may become
exacerbated by climate change. Figure 13-4 illustrates seasonal livelihood
sensitivity for the Lake Victoria Basin in East Africa (Gabrielsson et al.,
2012).
Shifts in livelihoods often occur due to changing climate trends, linked
to a series of environmental, socioeconomic, and political stressors
(robust evidence). Farmers may change their crop choices instead of
abandoning farming (Kurukulasuriya and Mendelsohn, 2007) or take on
more lucrative income-generating activities (see Figure 13-3). Uncertainty
about West Africa’s rainy season threatens small-scale farming and water
management (Yengoh et al., 2010a,b; Armah et al., 2011; Karambiri et
al., 2011; Lacombe et al., 2012). Around Mali’s drying Lake Faguibine,
livelihoods shifted from water-based to agro-sylvo-pastoral systems, as
a direct impact of lower rainfall and more frequent and more severe
droughts (Brockhaus and Djoudi, 2008). Diverse indigenous groups in
Russia have changed their livelihoods as result of Soviet legacy and
climate change; for example, many Viliui Sakha have abandoned cow-
keeping due to youth out-migration, growing access to consumer goods,
and seasonal changes in temperature, rainfall, and snow (Crate, 2013).
Under certain converging shocks and stressors, people adopt entirely
new livelihoods. In South Africa, higher precipitation uncertainty raised
reliance on livestock and poultry rather than crops alone in 80% of
households interviewed (Thomas et al., 2007). In southern Africa and
India, people migrated to the coasts, switching from climate-sensitive
farming to marine livelihoods (Coulthard, 2008; Bunce et al., 2010a,b).
After Hurricane Stan (2005), land-poor coffee farmers in Chiapas, Mexico,
turned from specializing in coffee to being day laborers and subsistence
farmers (Eakin et al., 2012).
13.2.1.3. Impacts on Poverty Dynamics:
Transient and Chronic Poverty
Limited evidence documents the extent to which climate change intersects
with poverty dynamics, yet there is high agreement that shifts from
transient to chronic poverty due to weather and climate are occurring,
especially after a series of weather or extreme events (Scott-Joseph,
2010). Households in transient poverty may become chronically poor
due to a lack of effective response options to weather events and
climate, compared with more affluent households (see Figure 13-3).
Often, multiple deprivations drive these shifts, with socially and
economically marginalized groups particularly prone to slipping into
chronic poverty. Women-headed households, children, people in informal
settlements (see Chapter 8), and indigenous communities are particularly
at risk, owing to compounding stressors such as lack of governmental
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Chapter 13 Livelihoods and Poverty
13
support, urban infrastructure, and insecure land tenure (see Section
13.2.1.5 and Chapter 12).
Poor people in urban areas in LICs and MICs in Africa, Asia, and Latin
America may slip from transient to chronic poverty given the combination
of population growth and flooding threats in low-elevation cities and
water stress in drylands (Balk et al., 2009) along with other multiple
deprivations (Mitlin and Satterthwaite, 2013). Poverty shifts also occur in
response to food price increases, though the strength of the relationship
between weather events and climate and food prices is still debated
(see Chapter 7 and Section 13.3.1.4). Poor households in urban and rural
areas are particularly at risk when they are almost exclusively net buyers
of food (Cranfield et al., 2007; Cudjoe et al., 2010; Ruel et al., 2010).
Misselhorn (2005) showed in a meta-study of 49 cases of food insecurity
in southern Africa that climatic drivers and poverty were the two
dominant and interacting causal factors. Poor pastoralists have collapsed
into chronic poverty when livestock assets have been lost (Thornton et
al., 2007). In rural areas, restricted forest access may exacerbate poverty
among already income-poor and elderly households who rely on forest
resources to respond to climatic shocks (Fisher et al., 2010). Yet, many
such shifts remain underexplored, incompletely captured in poverty data
and adaptation monitoring. The bulk of evidence in the literature is
oriented toward extreme events, rapid-onset disasters, and subsequent
impacts on livelihoods and poor people’s lives. Subtle changes are rarely
tracked, making quantification of long-term trends and detection of
impacts difficult.
13.2.1.4. Poverty Traps and Critical Thresholds
Poverty traps arise when climate change, variability, and extreme events
keep poor people poor and make some poor even poorer. Yet, attribution
remains a challenge. Among disadvantaged people in urban areas,
poverty traps are reported especially for wage laborers who erode their
financial capital due to increases in food prices (Ahmed et al., 2009;
Hertel and Rosch, 2010) and for those in informal settlements exposed
to floods and landslides (Hardoy and Pandiella, 2009). In rural areas,
poverty traps are reported when climate change impacts on poor people
persist over decades, such as through environmental degradation and
recurring stress on ecosystems in the Sahel (Kates, 2000; Hertel and
Rosch, 2010; Sissoko et al., 2011; UNCCD, 2011), or when people are
unable to rebuild assets after a series of stresses (Eriksen and O’Brien,
2007; Sabates-Wheeler et al., 2008; Sallu et al., 2010). Poverty traps and
destitution are also described in pastoralist systems, triggered through
droughts, restricted mobility owing to conflict and insecurity, adverse
terms of trade, and the conversion of grazing areas to agricultural land,
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Climate
Farm work
Diseases
Figure 13-4 | Seasonal sensitivity of livelihoods to climatic and non-climatic stressors for one calendar year, based on experiences of smallholder farmers in the Lake Victoria
Basin in Kenya and Tanzania (Gabrielsson et al., 2012).
807
Livelihoods and Poverty Chapter 13
13
s
uch as for biofuel production (Eriksen and Lind, 2009; Homewood, 2009;
Eriksen and Marin, 2011). Other poverty traps result from heavy debt
loads due to the inability to repay loans and distress sales (Renton, 2009;
Ahmed et al., 2012), persistent discrimination through legal structures
and formal institutions, especially for women and other marginalized
groups (Campbell et al., 2009; McDowell and Hess, 2012), and at the
nexus of climate, health, and conflict (see Chapter 10).
Despite limited evidence, there is high agreement that critical thresholds,
or irreversible damage (Heltberg et al., 2009), result from the convergence
of various factors, many of which are not directly related to climate
change. For instance, poor people often rely on social networks, including
reciprocal gifts and exchanges, to protect themselves from shocks and
crises such as droughts and illness (Little et al., 2006). Yet, given limited
assets and ability to mobilize labor and food, particularly for smaller
and women-headed households and the elderly, the exhaustion of these
reciprocal ties can indicate an imminent slipping into poverty traps or
chronic poverty (Pradhan et al., 2007; Osbahr et al., 2008). Injuries,
disabilities, disease, psychological distress, for example from accidents
during flood events, diminish poor people’s main asset, labor (Douglas
et al., 2008), and may plunge them into chronic poverty.
Few studies illustrate positive livelihood impacts as a result of climate
change or climate-induced shocks, and they often tend to refer to more
affluent and powerful constituencies. Very scarce evidence exists of poor
people escaping poverty traps (see Figure 13-3). In Cameroon, though,
f
arming communities benefit from occasional rainfall during the dry
season and more food stuffs while the drying of swamps allows maize
off season (Bele et al., 2013). In Lake Victoria Basin, collective action
has increased as a result of HIV/AIDS and climate change, boosting social
assets (Gabrielsson and Ramasar, 2012). Lessons from Hurricane Mitch
(1998) in Honduras point toward more equitable land distribution
and better flood preparedness that benefit the poor after disasters
(McSweeney and Coomes, 2011).
13.2.1.5. Multidimensional Inequality and Vulnerability
Climate variability and change as well as climate-related disasters
contribute to and exacerbate inequality, in urban and rural areas, in
LICs, MICs, and HICs. Mounting inequality is not just a side effect of
weather and climate but of the interaction of related impacts with
multiple deprivations at the context-specific intersections of gender,
age, race, class, caste, indigeneity, and (dis)ability, embedded in uneven
power structures, also known as intersectionality (Nightingale, 2011;
Kaijser and Kronsell, 2013; see Figure 13-5). This section illustrates how
climate impacts intersect with inequality, primarily along the lines of
gender, age, and indigeneity. Other chapters are referenced.
Medium evidence highlights impacts of climate stresses and extreme
events on children (Cutter et al., 2012; O’Brien et al., 2012). Children in
urban slums suffer from inadequate water supplies and malnutrition, which
Climate change and
climate change responses
Socioeconomic
development pathways
Capacities and
opportunities
Resilient
HighLow
Privileged
At risk
Marginalized
Multidimensional
vulnerability
Class
Gender
Age
Ethnicity
(Dis)ability
Age
(Di
s)a
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ity
Race
Identity markers
and dimensions of
inequality
Intersecting
dimensions of
inequality
Multidimensional vulnerability
Population
• Producing some privileged and resilient people with very little or no multidimensional
vulnerability
• Producing many marginalized and at risk people, with fewer capacities and opportunities,
and higher multidimensional vulnerability
• Many people in between
Figure 13-5 | Multidimensional vulnerability driven by intersecting dimensions of inequality, socioeconomic development pathways, and climate change and climate change
responses. Vulnerability depends on the structures in society that trigger or perpetuate inequality and marginalization—not just income-poverty, location, or one dimension of
inequality in itself, such as gender.
808
Chapter 13 Livelihoods and Poverty
13
e
xacerbates impacts from heat stress, while excessive rain heightens
water-borne diseases (Bartlett, 2008). Flood-related mortality in Nepal
was twice as high for girls as for women (13.3 per 1000 girls) and also
higher for boys than for men, and for young children in general six times
higher than before the flood (Pradhan et al., 2007). Lower caloric intake
due to two back-to-back droughts and price shocks in Zimbabwe in the
1980s resulted in physical stunting among children and reduced lifetime
earnings (Alderman, 2010). In Mali, the incidence of child food poverty
increased from 41% to 52% since the 2006 food price increases (Bibi
et al., 2010). See Chapter 11 for more details.
Health impacts of weather events and climate differentially affect the
elderly and socially isolated (Frumkin et al., 2008; see also Chapter 11).
In Vietnam the elderly, widows, and disabled people, in addition to
Box 13-1 | Climate and Gender Inequality: Complex and Intersecting Power Relations
Existing gender inequality (see Box CC-GC) is increased or heightened as a result of weather events and climate-related disasters
intertwined with socioeconomic, institutional, cultural, and political drivers that perpetuate differential vulnerabilities (robust evidence;
Lambrou and Paina, 2006; Adger et al., 2007; Brouwer et al., 2007; Shackleton et al., 2007; Carr, 2008; Demetriades and Esplen, 2008;
Galaz et al., 2008; Osbahr et al., 2008; Buechler, 2009; Nightingale, 2009; Terry, 2009; Dankelman, 2010; MacGregor, 2010; Alston,
2011; Arora-Jonsson, 2011; Resurreccion, 2011; Heckenberg and Johnston, 2012; Zotti et al., 2012; Alston and Whittenbury, 2013;
Rahman, 2013; Shah et al., 2013). While earlier studies have tended to highlight women’s quasi-universal vulnerability in the context
of climate change (e.g., Denton, 2002), this focus can ignore the complex, dynamic, and intersecting power relations and other
structural and place-based causes of inequality (Nightingale, 2009; UNFPA, 2009; Arora-Jonsson, 2011). Moreover, the construction of
economically poor women as victims denies women’s agency and emphasizes their vulnerability as their intrinsic problem (MacGregor,
2010; Manzo, 2010; Arora-Jonsson, 2011).
Gendered livelihood impacts: Men and women are differentially affected by climate variability and change. The 10-year drought in
Australia’s Murray-Darling Basin differentially affected men and women, owing to their distinct roles within agriculture (e.g., Eriksen
et al., 2010). Alston (2011) noted social disruption and depression, most profound in areas with almost total reliance on agriculture,
no substitute employment, and limited service infrastructure (Table 13-1). In India, more women than men, especially women of
lower castes, work as wage laborers to compensate for crop losses (Lambrou and Nelson, 2013) while in Tanzania, wealthier women
hire poorer women to collect animal fodder during droughts (Muthoni and Wangui, 2013). Climate variability amplifies food shortages
in which women consume less food (Lambrou and Nelson, 2013) and suffer from reproductive tract infections and water-borne
diseases after floods (Neelormi et al., 2008; Campbell et al., 2009). Women farmers in the Philippines relying on high-interest loans
were sent to jail after defaulting on debts following crop failure (Peralta, 2008). In Uganda, men were able to amass land after floods
while droughts reduced women’s non-land assets (Quisumbing et al., 2011). In Ghana, some husbands prevent their wives from
cultivating individual plots as a response to gradually shifting rainfall seasonality, thereby undermining both women’s agency and
household well-being (Carr, 2008).
Continued next page
Experiences Male farmers Female farmers
Increased
workload
Demanding tasks such as feeding livestock, carting water, destroying frail
animals (A)
Assistance with farm tasks and working off the farm for additional income (A)
Increased migration for wage labor, typically farther away from home (I) Increased collection of fi rewood and uptake of wage labor (especially lower
castes) in neighboring villages (I)
Community
interactions,
isolation, and
exploitation
Locked into farms, loss of political power (A) Increased interactions and caregiving work, taking care of others’ health at the
expense of their own (A)
Exploitation by labor contractors when migrating (I) Disadvantage in accessing institutional support and climate information (I)
Physical and
psychological
toll
Feel demonized (farmers seen as responsible for crisis), increased stress, social
isolation, depression, and high suicide levels (A)
Working lives appear indefi nite, resulting in increased stress (A)
Increased anxiety to provide food and access loans and escape trap of
indebtedness, increase in domestic fi ghts, sometimes suicide (I)
Increased pressure to provide food and save some more from sale for
consumption, less food intake, increase in domestic fi ghts (I)
Table 13-1 | Examples of gendered climate experiences.
(A) = Australia (ten-year drought, 2003 – 2012), based on Alston (2011); (I) = India (climate variability and changing climatic trends), based on Lambrou and Nelson
(2013).
809
Livelihoods and Poverty Chapter 13
13
single mothers and women-headed households with small children,
were least resilient to floods and storms and slow-onset events such as
recurrent droughts (Campbell et al., 2009). In Australia, older citizens
have shown feelings of distress as a result of familiar landscapes altered
by drought, loss of home gardens, social isolation, and physical harm
related to heat stress and wild fires (Pereira and Pereira, 2008; Horton et
al., 2010; Polain et al., 2011). Elderly citizens in the UK may underestimate
the risk and severity of heat waves through their social networks and
fail to act (Wolf et al., 2010). In the USA, Europe, and South Korea, the
elderly, children, and persons of lower socioeconomic status have a
heightened risk of heat-related mortality (Baccini et al., 2008; Balbus
and Malina, 2009; Son et al., 2012). Preliminary evidence suggests
Box 13-1 (continued)
Feminization of responsibilities: Campbell et al. (2009) and Resurreccion (2011), in case studies from Vietnam, found increased
workloads for both partners linked to weather events and climate, contingent on socially accepted gender roles: men tended to work
longer hours during extreme events and women adopted extra responsibilities during disaster preparation and recovery (e.g., storing
food and water and taking care of the children, the sick, and the elderly) and when their husbands migrated. In Cambodia, Khmer
men and women accepted culturally taboo income-generating activities under duress, when rice cropping patterns shifted due to
higher temperatures and more irregular rainfall (Resurreccion, 2011). Despite increased workloads for both sexes, women’s extra
work adds to already many labor and caring duties (Nelson and Stathers, 2009; MacGregor, 2010; Petrie, 2010; Arora-Jonsson, 2011;
Kakota et al., 2011; Resurreccion, 2011; Muthoni and Wangui, 2013; Shah et al., 2013). In Nepal, shifts in the monsoon season, longer
dry periods, and decreased snowfall push Dalit girls and women (“untouchable” caste) to grow drought-resistant buckwheat and
offer more day labor to the high caste Lama landlords while Dalit men seek previously taboo patronage protection to engage in
cross-border trade (Onta and Resurreccion, 2011). Rising male out-migration, for example, in Niger and South Africa, leave women
with all agricultural tasks yet limited extra labor (Goh, 2012). Additional workloads exhaust women emotionally and physically,
shown in South Africa (Babugura, 2010).
Occupational hazards: Increasing cases of heat death are reported among male workers on sugarcane plantations in El Salvador
due to kidney failure (Peraza et al., 2012) and heat-related indoor work emergencies in Spain among young (<50 years) able-bodied
urban men (García-Pina et al., 2008). Anecdotal evidence suggests that women tea pickers in Malawi, Kenya, India, and Sri Lanka suffer
and die from heat stress as payment by quantity discourages rest breaks (Renton, 2009; see also Chapter 11 and CC-HS). In cases of
male out-migration due to unsustainable rural livelihoods, women in Bangladesh face unsafe working conditions, exploitation, and
loss of respect (Pouliotte et al., 2009). Yet, male out-migration could provide opportunities for women to move beyond traditionally
constrained roles, explore new livelihood options, and access public decision-making space (CIDA, 2002; Fordham et al., 2011).
Emotional and psychological distress: Climate-related disasters or gradual environmental deterioration can affect women’s mental
health disproportionally due to their multiple social roles (UN ECLAC, 2005; Babugura, 2010; Boetto and McKinnon, 2013; Hargreaves,
2013). Increased gender-based violence within households is reported as an indirect social consequence of climate-related disasters,
as well as slow-onset climate events, owing to greater stress and tension, loss and grief, and disrupted safety nets, reported for
Australia (Anderson, 2009; Alston, 2011; Parkinson et al., 2011; Hazeleger, 2013; Whittenbury, 2013), New Zealand (Houghton,
2009), the USA (Jenkins and Phillips, 2008; Anastario et al., 2009), Vietnam (Campbell et al., 2009), and Bangladesh (Pouliotte et al.,
2009).
Mortality: Social conditioning affects mortality for women and men. Rahman (2013) and Nellemann et al. (2011) confirm patterns of
gender disparity with respect to swimming that contribute to high number of female deaths due to climate-related disasters. Restricted
mobility keeps women in Bangladesh and Nicaragua waiting in risk-prone houses during floods (Saito, 2009; Bradshaw, 2010). Some
disaster relief structures that lack facilities appropriate for women may contribute to increased harm and mortality (World Bank,
2010). When they are socioeconomically disadvantaged and the disasters exacerbate existing patterns of discrimination, more
women die in hurricanes and floods (Neumayer and Plümper, 2007; Ray-Bennett, 2009). Yet, men experience a higher mortality rate
when fulfilling culturally imposed roles as heroic life-savers (Röhr, 2006; Campbell et al., 2009; Resurreccion, 2011).
810
Chapter 13 Livelihoods and Poverty
13
d
ifferential harm of 2012 Superstorm Sandy in New York, observed
among elderly people and medically underserved populations (Pagán
Motta, 2013; Teperman, 2013; Uppal et al., 2013).
Inequality and disproportionate effects of climate-related impacts also
occur along the axes of indigeneity and race. Disproportionate climate
impacts are documented for Afro-Latinos and displaced indigenous
groups in urban Latin America (Hardoy and Pandiella, 2009), and
indigenous peoples in the Russian North (Crate, 2013) and the Andes
(Andersen and Verner, 2009; Valdivia et al., 2010; McDowell and Hess,
2012; Sietz et al., 2012). See Chapter 12 for impacts on indigenous
cultures. In the USA, low-income people of color are more affected by
climate-related disasters (Sherman and Shapiro, 2005; Morello-Frosch
et al., 2009; Lynn et al., 2011) as demonstrated in the case of low-
income African American residents of New Orleans after Hurricane
Katrina (Elliott and Pais, 2006).
13.2.2. Understanding Future Impacts of and Risks from
Climate Change on Livelihoods and Poverty
Future climate change, as projected through modeling, will continue to
affect poor people in rural and urban areas in LICs, MICs, and HICs, alter
their livelihoods, and make efforts to reduce poverty more difficult (high
confidence). Studies reveal a broad range of impacts for the near-
(2030–2040) and long-term (2080–2100) future, depending on the
climatic, agro-economic, and demographic models employed, their key
variables, and spatial scale, which vary from a country’s agro-ecological
zones to the global. Few projections take into account policy options or
adaptation.
Projections emphasize the complexity and heterogeneity of future
climate impacts, including winners and losers in close geographic
proximity. Anticipated impacts on the poor are expected to interact with
multiple stressors, most notably social vulnerability (Iglesias et al.,
2011), low adaptive capacity and subsistence constraints under chronic
poverty (Liu et al., 2008), weak institutional support (Menon, 2009; Xu
et al., 2009; Skoufias et al., 2011a,b), population increases (Müller et
al., 2011), natural resource dependence (Adano et al., 2012), ethnic
conflict and political instability (Challinor et al., 2007; Adano et al., 2012),
large-scale land conversions (Assuncao and Cheres, 2008; Thornton et
al., 2008), and inequitable trade relations (Challinor et al., 2007; Jacoby
et al., 2011).
Table 13-2 illustrates estimated risks and adaptation potentials for
livelihoods and poverty dimensions until 2100.
13.2.2.1. Projected Risks and Impacts by Geographic Region
Climate change will exacerbate risks and in turn further entrench
poverty (very high confidence). The well-known and highly referenced
Wheeler data set (2011) analyzes climate risk and coping ability by
country. Future increases in the frequency of extreme events are overlaid
with considerable poverty, although not all poor people will be at risk.
Of the 20 countries and regions most at risk, seven are LICs (Bangladesh,
Ethiopia, Kenya, Madagascar, Mozambique, Somalia, and Zimbabwe),
e
ight are LMICs (Bolivia, Djibouti, Honduras, India, Philippines, Sri
Lanka, Vietnam, and Zambia), four are UMICs (China, Colombia, Cuba,
and Thailand), and one is an HIC (Hong Kong). For China, Djibouti, India,
Kenya, and Somalia, climate contributes between 46.4% and 87.5% to a
2008–2015 rise in national risk, compared to income and urbanization.
Highest sensitivity to sea level rise by 2050, based on low-elevation
coastal zones, population density, and areas of storm surge zones, is
expected for India, Indonesia, China, the Philippines, and Bangladesh.
India and Indonesia are projected to experience a 80% and 60%
increase, respectively, in their populations at risk from sea level rise,
housing a combined total of more than 58 million people most at risk
by 2050; 6 million people more at risk from sea level rise in China will
bring its total to 22 million, and Bangladesh’s at-risk population is
predicted to grow to 27 million—more than double since 2008 (Wheeler,
2011).
Specific regions at high risk are those exposed to sea level rise and
extreme events and with concentrated multidimensional poverty,
including pockets of poor people in LICs and MICs: mega-deltas in
Bangladesh, Thailand, Myanmar, and Vietnam (Eastham et al., 2008;
Wassmann et al., 2009), drylands (Anderson et al., 2009; Piao et al.,
2010; Sietz et al., 2011), mountain areas (Beniston, 2003; Valdivia et
al., 2010; Gentle and Maraseni, 2012; Gerlitz et al., 2012; McDowell and
Hess, 2012), watersheds in the Himalayas (Xu et al., 2009), ecologically
fragile areas in China (Taylor and Xiaoyun, 2012), coastal areas with
severe ecosystem deterioration in eastern and southern Africa (Bunce
et al., 2010a,b), and river deltas subject to resource extraction (Syvitski
et al., 2009).
13.2.2.2. Anticipated Impacts on Economic Growth
and Agricultural Productivity
Most projected future impact studies focus on the long-term effects of
climatic changes and shocks on agricultural productivity, mainly in
Africa, Asia, and Latin America. They typically examine impacts on
economic growth (see also Chapter 10), changes in food prices and food
security, and extrapolated changes in poverty head counts.
For future poverty head counts caused by climate change, the literature
shows disagreement. For the very near future, a study by Thurlow et al.
(2009) estimates that, by 2016, Zambia’s poverty headcount would
increase by 300,000 people under average climate variability, and by
650,000 under a worst 10-year rainfall sequence. Skoufias et al. (2011b),
using 2055 predictions based on the Nordhaus (2010) RICE (Regional
dynamic Integrated model of Climate and the Economy) model, state
that under business-as-usual and optimal abatement, global poverty
(measured at $2 per day) could be reduced by 800 million people, owing
to annual and real per capita growth rate of 2.2% up to 2055. However,
lower probability extreme events would reverse this trend, and mitigation
under optimal abatement typically excludes people living in poverty
(Skoufias et al., 2011b).
In contrast, Tubiello et al. (2008) project that, by 2080, the number of
undernourished people may increase by up to 170 million, using the A2
Special Report on Emission Scenarios (SRES) scenarios, and up to a total
of 1300 million people assuming no carbon dioxide (CO
2
) fertilization.
811
Livelihoods and Poverty Chapter 13
13
Projections of future climate change impacts on gross domestic product
(GDP) use non-disaggregated poverty data. For instance, Mendelsohn
et al. (2006) use dynamic coupled ocean-atmosphere models and market
response functions to simulate the distribution of climate impacts for
2100. Independent of the climate scenarios, poor countries, mainly in
Africa and Southeast Asia, will face the largest losses (0.2 to 1.2%
reduction in GDP) and, under experimental models, up to 23.8% drop
in GDP; in contrast, the richest quartile will encounter both positive and
negative effects, ranging –0.1% to +0.2% GDP, and up to a 0.9% GDP
increase under experimental models. Changes in GDP reflect climate-
sensitive economic sectors, especially water and energy, with poor
nations in low latitudes already facing high temperatures and thus more
Key risk
Adaptation issues & prospects
Climatic
drivers
Risk & potential for
adaptation
Timeframe
D
amaging
c
yclone
Climate-related drivers of impacts
W
arming
t
rend
E
xtreme
p
recipitation
E
xtreme
t
emperature
S
ea
l
evel
L
evel of risk & potential for adaptation
Potential for additional adaptation
to reduce risk
R
isk level with
c
urrent adaptation
R
isk level with
h
igh adaptation
D
rying
t
rend
Present
2°C
4°C
V
ery
low
V
ery
high
M
edium
Present
2
°C
4°C
Very
l
ow
Very
h
igh
Medium
Present
2°C
4°C
Very
low
Very
high
M
edium
Present
2°C
4°C
Very
low
Very
high
Medium
Present
2°C
4°C
Very
low
Very
high
Medium
Present
2°C
4°C
Very
low
Very
high
Medium
N
ear term
(2030 – 2040)
L
ong term
(2080 – 2100)
N
ear term
(2030 – 2040)
Long term
(2080 – 2100)
Near term
(2030 – 2040)
Long term
(2080 – 2100)
Near term
(2030 – 2040)
Long term
(2080 – 2100)
Near term
(2030 – 2040)
Long term
(2080 – 2100)
Near term
(2030 – 2040)
Long term
(2080 – 2100)
Deteriorating livelihoods in drylands, due to high and
p
ersistent poverty. Risk of reaching tipping points for
crop and livestock production in small-scale farming
and/or pastoralist livelihoods (high confidence)
[13.2.1.2, 13.2.2.1, 13.2.2.3]
Adaptation options are limited owing to persistent poverty,
declining land productivity, food insecurity, and limited government
support due to marginalization. Rural–urban migration is a potential
a
daptation strategy.
Destruction and deterioration of assets: physical (homes,
l
and, and infrastructure), human (health), social (social
networks), cultural (sense of belonging and identity),
and financial (savings) due to floods in flood-prone
a
reas, such as low-lying deltas, coasts, and small islands
(high confidence)
[13.2.1.1, 13.2.1.3, 13.2.1.5, Box 13-1]
Adaptation options are limited for people who cannot afford
r
elocation to safer areas. Government support and private options
(
e.g., insurance) are limited for people with insecure or unclear
tenure.
Shifts from transient to chronic poverty due to persistent
economic and political marginalization of poor people
combined with deteriorating food security
(high confidence)
[13.2.1.3, 13.2.2.4]
Adaptation options are limited due to exclusion from markets and
low government support. Policies for adaptation are unsuccessful
because of failure to address persistent inequalities.
Declining work productivity, morbidity (e.g., dehydration,
heat stroke, and heat exhaustion), and mortality from
exposure to heat waves. Particularly at risk are agricultural
and construction workers as well as children, homeless
people, the elderly, and women who have to walk long
hours to collect water (high confidence)
[13.2.1.1, 13.2.1.5, 13.2.2.4, Box 13-1]
Adaptation options are limited for people who are dependent on
agriculture and too poor to afford agricultural machinery.
Adaptation options are limited in the construction sector where
many poor people work under insecure arrangements. Adaptation
might be impossible in certain areas in a +4°C world.
Declining agricultural yields, primarily in already hot
climates, with severe impacts on countries and
communities highly dependent on agriculture. Declining
yields may cause further deterioration of assets: financial
(savings), human (health), social (social networks), and
cultural (sense of belonging and identity) (high confidence)
[13.2.2.2, 13.2.2.4]
Adaptation by changing livelihoods away from agriculture is limited
owing to poverty and marginalization. Adaptation strategies such as
early or late planting, inter-cropping, and shifting crops bring mixed
benefits and have limitations, often depending on household
resources and access to seasonal forecasts and longer term
projections. In a +4°C world, adaptation in agriculture is very
limited.
Reduced access to water for rural and urban poor
people due to water scarcity and increasing competition
for water (high confidence)
[13.2.1.1, 13.2.1.3, 13.2.1.5, Box 13-1]
Adaptation through reducing water use is not an option for the
large number of people already lacking adequate access to safe
water. Access to water is subject to various forms of discrimination,
for instance due to gender and location. Poor and marginalized
water users are unable to compete with water extraction by
industries, large-scale agriculture, and other powerful users.
Table 13-2 | Key risks from climate change for poor people and their livelihoods and the potential for risk reduction through adaptation. Key risks are identified based on
assessment of the literature and expert judgment by chapter authors, with evaluation of evidence and agreement in the supporting chapter sections. Each key risk is characterized
as very low, low, medium, high, or very high. Risk levels are presented in three timeframes: present, near-term (2030–2040), and long term (2080–2100). Near term indicates
that projected levels of global mean temperature do not diverge substantially across emissions scenarios. Long term differentiates between a global mean temperature increase of
2°C and 4°C above preindustrial levels. For each timeframe, risk levels are estimated for a continuation of current adaptation and for a hypothetical highly adaptive state. Bars
that only show the latter indicate a limit to adaptation (see Chapter 16). Relevant climate variables are indicated by symbols. This table should not be used as a basis for ranking
severity of risks.
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Chapter 13 Livelihoods and Poverty
13
v
ulnerable to decreased agricultural productivity with increased warming.
One study for the USA, using the SRES A2 scenario, projects that four
climate change impacts—hurricane damage, energy costs, water costs,
and real estate—are expected to cost 1.8% of the country’s GDP by
2100, leading to higher household costs for basic necessities such as
energy and water (Ackerman et al., 2008). Groups that spend the
highest proportion of their income on these necessities will be
disproportionately affected.
A growing body of literature estimates future changes in agricultural
production and food prices due to climate change, variability, and
extreme events (Slater et al., 2007; Thomas et al., 2007; Assuncao and
Cheres, 2008; Burke et al., 2011; see Chapters 7 and 9, and Box CC-HS).
Mixed trends are projected for major staples for all continents until the
mid-21st century. For the near-term future, the production of coarse
grains in Africa may be reduced by 17 to 22% due to climate change;
well-fertilized modern seed varieties are projected to be more susceptible
to heat stress than traditional ones (Schlenker and Lobell, 2010). By
2080, a major decrease in land productivity is expected for sub-Saharan
Africa (–14% to –27%) and Southeast Asia (–18% to –32%), coupled
with increase in water demand, while lowest risks are projected for
North America, Europe, East Asia, Russia, and Australia (Iglesias et al.,
2011).
13.2.2.3. Implications for Livelihood Assets,
Trajectories, and Poverty Dynamics
Projections of near- and long-term climate change impacts on livelihood
assets highlight the erosion of financial assets as a result of increased
food prices (Ahmed et al., 2009; Seo et al., 2009; Thurlow et al., 2009;
Hertel et al., 2010; Jacoby et al., 2011; Skoufias et al., 2011b), human
assets due to decline in nutritional status (Liu et al., 2008), and natural
assets due to lower agricultural productivity (Jones and Thornton,
2009;Thurlow et al., 2009; Skoufias et al., 2011b). They also show a
substantial increase in future heat-related mortality (Basu and Samet,
2002; McGregor et al., 2006; Sherwood and Huber, 2010; Huang et al.,
2011), increasing infectious disease transmission rates (Green et al., 2010),
and other health impacts (see Chapter 11). Impacts on social and
cultural assets have received little attention. Exceptions address losses
of social identity and cultural connections with land and sea among
indigenous populations threatened by sea level rise and potential
relocation (Green et al., 2010) and conflicts between ethnic and/or
religious groups (Adano et al., 2012; see also Chapter 12). Poor households
with limited social networks will be worst off, including in places such
as Nepal (Menon, 2009) and Indonesia (Skoufias et al., 2011a).
Climate change is also projected to cause shifts in livelihood trajectories.
In Mali’s agricultural-pastoralist transition zone, due to temperature
increase and drying projected for 2025 and coupled with a 50% increase
in population, shifts from rainfed millet and sorghum to semiarid,
predominantly livestock subsistence are expected to expose an extra
6 million people to malnutrition, including 250,000 children suffering
from stunting (Jankowska et al., 2012). Simulated probabilities of failed
seasons, using current daily rainfall data and 2050 projections for the
length of growing period, show transitions from cropping to livestock
in other marginal cropping areas in Africa (Thomas et al., 2007; Jones
a
nd Thornton, 2009). The Met Office Hadley Centre climate prediction
model 3 (HadCM3) and a high emission scenario (SRES A1F1) show
that, by 2050, expanding vector populations, especially tsetse, and a
greater than 20% decline in growing period, in livestock-dependent and
mixed crop-livestock livelihoods in semiarid to arid Africa and Asia,
combined with increasing water scarcity and stover loss due to maize
substitution (Thornton et al., 2007) will stress livelihoods of poor farmers
and pastoralists.
Future climate change impacts on disaggregated poverty are addressed
mainly through projected changes in food prices and earnings associated
with impacts on agricultural production (Schmidhuber and Tubiello,
2007). Changes in price-induced earnings lower the welfare of low-
income households, particularly urban and wage-labor dependent
households that use a large income share to purchase staple crops. In
the near-term future, under low productivity scenarios assuming rapid
temperature increase by 2030, poverty among the agricultural self-
employed in 15 LICs and MICs may drop due to benefits from selling
surplus production at higher prices, by as much as 40% in Chile and the
Philippines; however, higher food prices may lead to a drop in national
welfare, as steep as 55% in South Africa (Hertel et al., 2010). In most
LICs and MICs, the poverty headcount is expected to drop in some
occupational strata and increase in others; only in most African countries
are yield impacts expected to be too severe to allow benefits (Hertel
and Rosch, 2010). Long-term, a one-time maximum extreme dry event,
simulated for 1971–2000 and 2071–2100 using the IPCC-SRES A2
scenario for 16 LICs and MICs, shows a 95 to 110% rise in poverty for
urban wage groups in Malawi, Zambia, and Mexico, while self-employed
farming households consolidate assets and face the smallest increase
in vulnerability (Ahmed et al., 2009). By 2100, climate change would
leave low-income, minority, and politically marginalized groups in
California’s agriculture with fewer economic opportunities, based on
SRES B1 and A1FI scenarios, particularly in dairy and grape production
(Cordova et al., 2006; Shonkoff et al., 2011).
13.2.2.4. Impacts on Transient and Chronic Poverty,
Poverty Traps, and Thresholds
Existing projections do not provide robust evidence to estimate whether
shifts from transient to chronic poverty will occur as a result of climate
change, and to what extent. However, a predicted increase in the
number of urban poor, especially wage laborers, suggests that a large
number may shift from transient to chronic poverty owing to exposure
to food price increases, or find themselves in a poverty trap, especially
under scenarios with long-duration climatic shifts and prolonged
droughts (Ahmed et al., 2009; Hertel et al., 2010). In Zambia, almost
half of the 650,000 new poor under the worst historic 10-year period
projected until 2016 are expected to be in urban areas while rural
poverty remains high (Thurlow et al., 2009). In Tanzania, Ahmed et al.
(2011), based on a high precipitation volatility General Circulation
Model (GCM), predict up to 1.17 million new poor into the near-term
future (up to 2031). Shifts in and out of poverty may occur by 2050 for
small-scale coffee farmers in Central America, as suitable coffee
growing areas move to higher altitudes, especially when constrained
by unequal access to agro-technical and climatic information (Laderach
et al., 2011).
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Livelihoods and Poverty Chapter 13
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P
oor countries will face greater poverty as a result of climate change
and extreme events (medium confidence), owing to location and low-
latitude high temperatures (Mendelsohn et al., 2006) anticipated further
decline in adaptive capacity combined with reductions in agricultural
productivity (Iglesias et al., 2011), greater inequality and deep-rooted
poverty (Jones and Thornton, 2009), and lower levels of education and
large numbers of young dependents (Skoufias et al., 2011c). Although
robust projections on poverty traps are lacking, they may be associated
with emerging hotspots of hunger, such as those projected for Tanzania,
Mozambique, and the Democratic Republic of Congo (DRC) by 2030
(Liu et al., 2008). Based on SRES scenarios, Devitt and Tol (2012) project
long-term coupled climate change- and conflict-induced poverty traps
for the DRC and several other sub-Saharan countries.
Some climate change projections (see Box CC-HS and WGI AR5 Chapters
11, 12, 14) indicate the possibility of large impacts that may exceed
thresholds of detrimental shocks to livelihoods and poverty, unless
strong adaptation and/or mitigation responses are implemented in a
timely manner (Kovats and Hajat, 2008; Sherwood and Huber, 2010).
Because women do most of the agricultural work, they will suffer
disproportionally from heat stress; for instance, in parts of Africa,
women carry out 90% of hoeing and weeding and 60% of harvesting
work (Blackden and Wodon, 2006). Toward the end of the century, the
risk of heat stress may become acute in parts of Africa, particularly the
Sahel, and the Indian sub-continent, potentially preventing people from
practicing agriculture (Patricola and Cook, 2010; Dunne et al., 2013). In
the glacier-dependent Himalayan region, excessive runoff and flooding
will threaten livelihoods (Xu et al., 2009). Relocation would represent
a critical threshold for indigenous groups, due to sea level rise for the
Torres Strait Islanders between Australia and Papua New Guinea (Green
et al., 2010) and permafrost degradation and higher and seasonally
erratic precipitation for the Viliui Sakha in the Russian North (Crate, 2013).
13.3. Assessment of Impacts of Climate Change
Responses on Livelihoods and Poverty
Climate change responses interact with social and political processes
to affect sustainable development and climate resilient pathways and
i
n turn, livelihoods and poverty. Climate mitigation and adaptation
responses include formal policies by governments, non-governmental
organizations (NGOs), bilateral and multilateral organizations, as well
as actions by individuals and communities. Such policy responses were
designed to have positive effects on sustainable development or at least
be neutral in terms of unintended side effects. Yet, much of the peer-
reviewed literature scrutinizing these responses suggests otherwise. This
section reviews empirical evidence of impacts of particular mitigation
(Section 13.3.1) and adaptation (Section 13.3.2) responses in the context
of livelihood and poverty trajectories and inequalities. Some of this
evidence is preliminary as several policies are still in their infancy while
other cases fail to assess multidimensional poverty or dynamic livelihood
decision making in the context of climate change responses.
13.3.1. Impacts of Mitigation Responses
Many synergies between climate change mitigation policies and poverty
alleviation have been identified in the literature (Klein et al., 2005; Ürge-
Vorsatz and Tirado Herrero, 2012), but evidence of positive outcomes
is limited. Impacts of current mitigation policies on livelihoods and
poverty are controversial with polarized views on the potential of such
policies for sustainable development in general and poverty alleviation
in particular (Collier et al., 2008; Böhm, 2009; Hertel and Rosch, 2010;
Michaelowa, 2011). This section assesses the observed and potential
impacts of four climate change responses on livelihoods and poverty:
the two mitigation responses most significant for poverty alleviation
under the United Nations Framework Convention on Climate Change
(UNFCCC), the Clean Development Mechanism (CDM) and Reduction
of Emissions from Deforestation and Forest Degradation (REDD+), and
two mitigation responses outside of the UNFCCC, voluntary carbon
offsets and biofuel production.
13.3.1.1. The Clean Development Mechanism
The CDM (see WGIII AR5 Chapter 13) aims to promote sustainable
development, thus CDM projects require approval by the host country’s
designated national authority. CDM projects as diverse as low-cost
Frequently Asked Questions
FAQ 13.3 | Are there unintended negative consequences of climate change policies
for people who are poor?
Climate change mitigation and adaptation policies may have unintended and potentially detrimental effects on
poor people and their livelihoods (the set of capabilities, assets, and activities required to make a living). Here is
just one example. In part as a result of climate change mitigation policies to promote biofuels and growing concern
about food insecurity in middle- and high-income countries, large-scale land acquisition in Africa, Southeast Asia,
and Latin America has displaced small landholders and contributed to food price increases. Poor urban residents
are particularly vulnerable to food price increases as they use a large share of their income to purchase food. At
the same time, higher food prices may benefit some agricultural self-employed groups. Besides negative impacts
on food security, biofuel schemes may also harm poor and marginalized people through declining biodiversity,
reduced grazing land, competition for water, and unfavorable shifts in access to and control over resources. However,
employment in the biofuel industry may create opportunities for some people to improve their livelihoods.
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Chapter 13 Livelihoods and Poverty
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e
nergy services in India, micro-hydro projects in Bhutan and Peru,
efficient firewood use in Nigeria, and biogas digesters in China and
Vietnam, are expected to generate livelihood benefits and employment,
and reduce poverty among beneficiaries (UNFCCC, 2011, 2013). The
secretariat’s own assessment of the CDM’s development benefits along
15 indicators suggested much room for improvement (UNFCCC, 2011).
Most of the statistical information in official reports on CDM is based
either on project documents or on surveys of project personnel rather
than in-depth studies.
The assessment of the CDM in the peer-reviewed literature is more
cautious and pessimistic than UNFCCC, and three reviews (Olsen, 2007;
Sutter and Parreño, 2007; Michaelowa and Michaelowa, 2011) contend
that the current CDM design is neither pro-poor nor contributes to
sustainable development. One reason for the low performance on
sustainable development criteria is that the CDM does not have any
requirements for monitoring and verification of development impacts
as required for emissions reductions (Boyd et al., 2009). Critiques entail
obstacles and ethical dilemmas in carbon trading (Liverman, 2009; Newell
and Bumpus, 2012), difficulties with implementation (Borges da Cunha
et al., 2007; Minang et al., 2007; Gong, 2010), procedural limitations
(Lund, 2010), and carbon offset goals favored over poverty reduction
goals (Wittman and Caron, 2009). While some authors claim that the
CDM undermines local and non-governmental input (Shin, 2010;
Corbera and Jover, 2012), others stress its transparency, including the
voices of local stakeholders (Michaelowa et al., 2012). Also, the CDM
may compete with the informal sector (Newell and Bumpus, 2012) and
accentuate uneven development by eroding local livelihood security
(Boyd and Goodman, 2011). In a meta-analysis of 114 CDM projects,
Crowe (2013) conclude that fewer than 10% of CDM projects had
successfully delivered pro-poor benefits and only one of them had
positive ratings on all seven criteria for pro-poor benefits. Among the
most promising examples are CDM projects in India supporting
community-designed plans to strengthen participation of marginalized
groups (Boyd and Goodman, 2011; Subbarao and Lloyd, 2011).
13.3.1.2. Reduction of Emissions from Deforestation
and Forest Degradation
Experience with REDD+ and other forest carbon projects is inadequate
to permit generalizations about effects on livelihoods and poverty
(Cotula et al., 2009; Hayes and Persha, 2010; Springate-Baginski and
Wollenberg, 2010; see Chapter 9). A study of 20 avoided deforestation
projects prior to REDD+ in Latin America, Africa, and Asia shows that
only five conducted some outcome or impact assessment, revealing a
lack of rigor in evaluation (Caplow et al., 2011). Despite optimism in
policy analyses about the potential of REDD+ for poverty alleviation
(Angelsen et al., 2009; Kanowski et al., 2011; Rahlao et al., 2012; Somorin
et al., 2013), there is growing evidence and high agreement in the peer-
reviewed literature that REDD+ may not lead to poverty alleviation and
that there may even be negative consequences. Concerns include threats
to the poor (Ghazoul et al., 2010; Phelps et al., 2010; Börner et al., 2011;
Larson, 2011; McDermott et al., 2011; Van Dam, 2011; Mahanty et al.,
2012; Neupane and Shrestha, 2012) and indigenous peoples (Shankland
and Hasenclever, 2011). Latent negative impacts include exclusion of local
people from forest use, and loss of local ownership in documenting the
s
tate of forests due to external monitoring and verification mechanisms
(Gupta et al., 2012; Pokorny et al., 2013). Benefit flows may be unevenly
distributed with regards to ethnicity (Krause and Loft, 2013), gender
(Peach Brown, 2011; UN-REDD, 2011), or simply not target the poor (Hett
et al., 2012). The absence of a global REDD+ mechanism means that
progress on REDD+ may occur as much through voluntary bilateral and
public-private processes as through multilateral, regulatory requirements
(Agrawal et al., 2011). Positive future benefits for poor people from
REDD+ will require attention to tenure and property rights, gender
interests, and community engagement (Danielsen et al., 2011; Mustalahti
et al., 2012).
The 2010 Cancun Agreements highlight safeguards for governments to
observe in REDD+ implementation, such as respect for the interests,
knowledge, rights, and sustainable livelihoods of communities and
indigenous peoples. If these safeguards will be observed in practice is
unclear owing to the early implementation state of REDD+ in most
countries as well as the uncertainty of the future of the global carbon
market (Lohmann, 2010; Savaresi, 2013).
13.3.1.3. Voluntary Carbon Offsets
The voluntary carbon offset (VCO) market is significant from a livelihoods
and poverty perspective because it typically targets smaller projects and
may be better at reaching poor communities (Estrada and Corbera,
2012), though it is modest in size compared to the regulated market
(approximately 1%). Also, those involved in the VCO market, namely
individuals, companies, organizations, and countries that have not ratified
the Kyoto Protocol, are often more willing to pay for carbon offsets with
co-benefits such as poverty alleviation (MacKerron et al., 2009).
Activities under VCO are dominated by renewable energy, primarily
wind power (30%), forestation projects including REDD+ (19%), and
methane destruction in landfills (7%) (Peters-Stanley and Hamilton,
2012). It is too early to tell whether these VCO projects are successful
in terms of poverty alleviation and other social goals, and results to
date are highly mixed (Jindal et al., 2008; Swallow and Meinzen-Dick,
2009; Jindal, 2010; Estrada and Corbera, 2012; Stringer et al., 2012).
Reported benefits include livelihood diversification, increased disposable
income, biodiversity conservation, and strengthening local organizations,
while exacerbated inequalities and loss of access to local resources are
known negative impacts (Estrada and Corbera, 2012). A study in Kenya,
Senegal, and Peru shows reduced losses of soil fertility in three soil
carbon sequestration projects, but also the inability of the poorest
farmers to participate and only marginal impacts on poverty reduction
(Antle and Stoorvogel, 2009). Out of 78 projects in 23 countries in
sub-Saharan Africa, only one promoted local social, economic, and
environmental benefits while the rest focused mainly on efficiency of
emission reductions (Karavai and Hinostroza, 2013).
13.3.1.4. Biofuel Production and Large-Scale Land Acquisitions
Biofuel production, often linked to transnational large-scale land
acquisitions (LSLA), is a near-term climate change mitigation response
that raises two major livelihood and poverty concerns: food price
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Livelihoods and Poverty Chapter 13
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i
ncreases and dispossession of land (see Chapters 4, 9). LSLA have
soared since 2008 (Von Braun et al., 2009; Borras Jr. et al., 2011a;
Deininger et al., 2011), partly linked to climate change responses
(medium evidence, high agreement). Biofuel production is considered
the primary driver, but there may be links to climate change through
high food prices (Daniel, 2011), food insecurity (Robertson and Pinstrup-
Andersen, 2010; Rosset, 2011; Sulser et al., 2011), and carbon markets
potentially raising land prices, for example, REDD+ (Cotula et al., 2009;
Zoomers, 2010; Anseeuw et al., 2012). LSLA global targets are biofuels
(40%), food (25%), and forestry (3%), with much regional variation
(Anseeuw et al., 2012). The IPCC special report on renewable energy
highlighted the uncertainties around the role of biofuels in food price
increases and risks of deteriorating food security with future deployment
of bioenergy (Edenhofer et al., 2011).
Increasing demand for biofuels shifts land from food to fuel production,
which may increase food prices (Collier et al., 2008) disproportionally
affecting the poor (Von Braun and Ahmed, 2008; Bibi et al., 2010; Ruel
et al., 2010). Despite high agreement that biofuel production plays a
role in food prices, little consensus exists on the size of this influence
(Aksoy and Isik-Dikmelik, 2008; Elobeid and Hart, 2008; Mitchell, 2008;
Von Braun and Ahmed, 2008; Baffes and Haniotis, 2010; Ajanovic, 2011;
Condon et al., 2013). Some studies link the 2007/2008 price spike to
speculation in agricultural futures markets (Runge and Senauer, 2007;
Ghosh, 2010) driven partly by potential future profits from biofuels
while their role was relatively less important in the 2010/2011 price
spike (Trostle et al., 2011).
LSLA have also triggered a land rush in LICs, which affects livelihood
choices and outcomes, with some distinct gender dimensions (Chu, 2011;
De Schutter, 2011; Julia and White, 2012; Peters, 2013). New competition
for land dispossesses smallholders, displaces food production, degrades
the environment, and pushes poor people onto more marginal lands less
adaptable to climatic stressors (Cotula et al., 2009; Borras Jr. et al., 2011a;
Rulli et al., 2013; Weinzettel et al., 2013). The expansion of bioenergy,
and biofuels in particular, increases the corporate power of international
actors over governments and local actors with harmful effects on national
food and agricultural policies (Dauvergne and Neville, 2009; Glenna and
Cahoy, 2009; Hollander, 2010; Mol, 2010; Fortin, 2011; Jarosz, 2012),
further marginalizing smallholders (Ariza-Montobbio et al., 2010; De
Schutter, 2011; Neville and Dauvergne, 2012) and indigenous peoples
(Montefrio, 2012; Obidzinski et al., 2012; Manik et al., 2013; Montefrio
and Sonnenfeld, 2013). There is growing apprehension that increased
competition for scarce land undermines women’s access to land and
their ability to benefit economically from biofuel investment (Arndt et
al., 2011; Chu, 2011; Molony, 2011; Behrman et al., 2012; Julia and
White, 2012; Perch et al., 2012). Concerns differ somewhat among
regions, with the greatest risk for negative outcomes for smallholders
in Africa (Daley and Englert, 2010; Borras et al., 2011b).
Mainstream economic modeling offers optimism that biofuels may
boost investment, employment, and economic growth in LICs such as
Mozambique (Arndt et al., 2009) and MICs such as India (Gopinathan
and Sudhakaran, 2011) and Thailand (Silalertruksa et al., 2012) yet
limited evidence exists on potential benefits being realized. A major
government initiative to promote jatropha cultivation in India has failed
(Kumar et al., 2011) and in some cases has left rural people worse off
(
Bastos Lima, 2012), whereas in Malawi it offered supplemental
livelihood opportunities (Dyer et al., 2012). Even though income and
employment in Brazil may have increased due to ethanol production
(Ferreira and Passador, 2011), structural inequalities in the sector remain
(Peskett, 2007; Hall et al., 2009; Bastos Lima, 2012). Biofuel production
in itself will not transform living conditions in rural areas without being
integrated into development policies (Hanff et al., 2011; Dyer et al.,
2012; Jarosz, 2012).
13.3.2. Impacts of Adaptation Responses
on Poverty and Livelihoods
Local responses to climate variability, shocks, and change have always
been part of livelihoods (Morton, 2007). Formal policy responses to
climate change, however, have developed more recently as the urgency
of adaptation, in addition to mitigation, became a clear international
policy mandate (Pielke Jr. et al., 2007). Even well-intentioned adaptation
projects (see Chapters 14 to 16) and efforts may have unintended and
sometimes detrimental impacts on livelihoods and poverty, and may
exacerbate existing inequalities. This section assesses the near-term
effects of autonomous and planned adaptation and formal insurance
schemes on the livelihoods of poor populations. Because adaptation
policies and projects are relatively recent, understanding of their long-
term effects is very limited.
13.3.2.1. Impacts of Adaptation Responses
on Livelihoods and Poverty
Autonomous adaptation strategies—such as diversification of livelihoods
(Smith et al., 2000; Mertz et al., 2009), migration (McLeman and Smit,
2006; Tacoli, 2009; see Chapter 12), storage of food (Smit and Skinner,
2002; Howden et al., 2007), communal pooling (Linnerooth-Bayer and
Mechler, 2006), market responses (Halstead and O’Shea, 2004); and
saving, credit societies, and systems of mutual support (Andersson and
Gabrielsson, 2012)—have been found to have positive effects on poverty
reduction in certain contexts, or at least prevent further deterioration
due to weather events and climate, especially when supported by policy
measures (Adger et al., 2003; Urwin and Jordan, 2008; Stringer et al.,
2009). Yet, some autonomous strategies such as diversification and
storage are often unavailable to the poorest, who lack the required
resources or surplus (Smithers and Blay-Palmer, 2001; Osbahr et al., 2008;
Seo, 2010) or require more labor-intensive practices that undermine
people’s health and may push them over a poverty threshold (Eriksen
and Silva, 2009). Moreover, autonomous adaptation strategies can
increase vulnerability for others or be subject to local elite capture
(McLaughlin and Dietz, 2008; Eriksen and Silva, 2009; Bhattamishra and
Barrett, 2010). Men’s migration in Northern Mali, for example, increases
the workload of the rest of the family, especially women, and reduces
children’s school attendance (Brockhaus et al., 2013). There is no evidence
regarding the impacts of autonomous responses on people living in
poverty in MICs and HICs.
Few rigorous studies about pilot adaptation projects exist outside of
organizations’ own assessments (Mapfumo et al., 2010; Nkem et al.,
2011) or evaluations of how planned adaptation was implemented or
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Chapter 13 Livelihoods and Poverty
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i
ntegrated into development (Gagnon-Lebrun and Agrawala, 2006; Gigli
and Agrawala, 2007). An assessment of the only completed Global
Environment Facility/World Bank (GEF/WB)-funded adaptation project,
in the Caribbean, Colombia, and Kiribati, did not directly appraise the
effects on poverty and livelihoods due to scarce baseline poverty data.
Other projects, such as in India’s Karnataka Watershed, are said to have
increased agricultural productivity, income, and employment, benefiting
the poorest and landless and improving equity (IEG, 2012). National
Action Plans of Adaptation tend to overemphasize technological and
infrastructural measures while often overlooking poor people’s needs,
gender issues, and livelihood and adaptation strategies (Agrawal and
Perrin, 2009; Perch, 2011).
13.3.2.2. Insurance Mechanisms for Adaptation
Insurance mechanisms (see Glossary and Chapter 10) reflect the tendency
that some formal adaptation measures reach the wealthier more easily
while prohibitive costs may prevent poor people from accessing such
mechanisms. Nonetheless, public and private insurance systems have
been proposed by the World Bank and UNFCCC as an adaptation
strategy to reduce, share, and spread climate change-induced risk and
smooth consumption, especially among poor households (Mechler et
al., 2006; Hertel and Rosch, 2010; Akter et al., 2011; Benson et al., 2012).
Formal insurance schemes can potentially provide a way out of poverty
traps (Barnett et al., 2008) caused by a household’s process to rebuild
assets after climate shocks over years (Dercon, 2006; Hertel and Rosch,
2010).
Poor people tend not to be insured via formal institutions, though strategies
such as risk spreading, social networks, local credit, asset markets, and
dividing herds between kin act as informal risk management mechanisms
(Barnett et al., 2008; Giné et al., 2008; Pierro and Desai, 2008; De Jode,
2010; Hertel and Rosch, 2010). Unable to access insurance, they often
invest in low-risk, low-return livelihood activities, which makes asset
accumulation to escape chronic poverty very difficult (Elbers et al., 2007;
Barnett et al., 2008). As a response, new insurance mechanisms such
as micro-insurance directed at low-income people and weather index
insurance for crops and livestock (see also Chapter 10) have emerged,
showing mixed results (Barnett et al., 2008; Mahul et al., 2009; Akter
et al., 2011; Matsaert et al., 2011; Biener and Eling, 2012).
Experiences from South Asia and several African countries illustrate
positive effects of micro-insurance on investment, production, and income
under drought and flood risk, including possible longer-term impacts
on future income-earning activities and health, although affordability
may limit the potential for the poorest (Yamauchi et al., 2009;
Hochrainer-Stigler et al., 2012; Karlan et al., 2012; Tadesse and Brans,
2012). There is emerging evidence that weather index insurance can
be specifically designed to reach the people usually uninsurable, for
example, by premium-for-work arrangements. In such arrangements
farmers provide labor and in return get an insurance certificate against
rain failure in a crucial growth period for their staple crops (Brans et
al., 2011). Slow uptake of insurance among poor people may be related
to farmers not fully understanding the schemes’ merits and function or
not trusting that payouts will come (Giné and Yang, 2009; Patt et al.,
2010).
13.4. Implications of Climate Change
for Poverty Alleviation Efforts
This section assesses how climate change may affect efforts to alleviate
p
overty. Evidence from observed impacts and projections highlight both
challenges and opportunities. The section builds on the findings from
Sections 13.1 to 13.3 and stresses the need to take into account the
complexity of livelihood dynamics, multidimensional poverty, and
intersecting inequalities to successfully navigate climate-resilient
development pathways (see Glossary).
Observed impacts of weather events and climate on livelihoods and
poverty and impacts projected from the subnational to the global level
suggest that livelihood well-being, poverty alleviation, and development
are already undermined and will continue to be eroded into the future
(high confidence). Climate change will slow down the pace of poverty
reduction, jeopardize sustainable development, and undermine food
security (high confidence; Hope, 2009;Stern, 2009; Thurlow et al., 2009;
Iglesias et al., 2011; Skoufias et al., 2011b). Currently poor and food-
insecure regions will continue to be disproportionately affected into the
future (high agreement; Challinor et al., 2007; Assuncao and Cheres,
2008; Lobell et al., 2008; Liu et al., 2008; Thornton et al., 2008; Jones
and Thornton, 2009; Menon, 2009; Nordhaus, 2010; Burke et al., 2011;
Jacoby et al., 2011; Skoufias et al., 2011a; Adano et al., 2012). Poorer
countries will experience declining adaptive capacity, which will hamper
development (high confidence). Posey (2009) flags lower adaptive
capacities in communities with concentrations of racial minorities and low-
income households than in more affluent areas, due to marginalization
and multidimensional inequality. Iglesias et al. (2011) project continental
disparities in agricultural productivity under progressively severe climate
change scenarios with highest risks for Africa and Southeast Asia.
Although there is high agreement about the heterogeneity of future
impacts on poverty, few studies consider more diverse climate change
scenarios (Skoufias et al., 2011b) or the potential of 4°C and beyond
(New et al., 2011). The World Bank (2012b, p. 65) states that “climate
change in a four degree world could seriously undermine poverty
alleviation in many regions.”
13.4.1. Lessons from Climate-Development Efforts
Two key models have attempted to integrate climate and poverty
concerns into development efforts: mainstreaming adaptation into
development priorities and pro-poor adaptation (see Chapters 14 to 16,
20). Lessons from “adaptation as development,” in which development
is seen as the basis for adaptation, and “adaptation plus development,”
in which development interventions address future climate threats
(Ayers and Dodman, 2010), typify the disagreement in policy spheres
about what sustainability constitutes (Le Blanc et al., 2012) and the
practical gulf between climate change policy and development spheres
(Ayers and Dodman, 2010). To date, observed and projected climate
change impacts are not systematically integrated into poverty reduction
programs, although such integration could result in substantial resilience
to covariate and idiosyncratic shocks and stresses (Brans et al., 2011;
Béné et al., 2012). At the same time, science and policy emphasis on
rapid-onset events, sectoral impacts, and poverty statistics has diverted
attention from threats to sustainability and resilient pathways. Even
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Livelihoods and Poverty Chapter 13
13
Box 13-2 | Lessons from Social Protection, Disaster Risk Reduction, and Energy Access
Social protection (SP): Considerable challenges emerge at the intersection of climate change adaptation, disaster risk reduction,
and social protection. SP programs include public and private initiatives that transfer income or assets to poor people, protect against
livelihood risks, and raise the social status and rights of the marginalized (see Glossary). Cash transfer programs are among the
principal instruments used by governments for poverty alleviation (Barrientos and Hulme, 2009; Barrientos, 2011; Niño-Zarazúa,
2011). There is medium agreement among scholars and practitioners that SP helps people in chronic poverty reduce risk and protect
assets during crises (Devereux et al., 2010, 2011; Barrientos, 2011; Dercon, 2011). At the regional and municipal level, SP often fails
to address local government capacity to ensure risk reduction by providing water, sanitation, drainage, health care, and emergency
services. Also, SP does not intentionally strengthen local collective capacity to proactively address climate change risks and take
action (Satterthwaite and Mitlin, 2013).
SP that supports pro-poor climate change adaptation and disaster risk reduction by strengthening the resilience of vulnerable
populations to shocks is labeled “adaptive social protection” (ASP) (Davies et al., 2009). ASP should be understood as a framework
rather than a package of specific measures. ASP has almost exclusively focused on LICs and some MICs with very little attention to
poor people in HICs. Few studies exist on the effectiveness of ASP for addressing incremental climatic changes and rapid-onset
events, and the changing nature of climate risks as part of dynamic livelihood trajectories (Heltberg et al., 2009; Arnall et al., 2010;
Bee et al., 2013). The Productive Safety Net Program in Ethiopia, for instance, had positive effects on household food consumption
and asset protection (Devereux et al., 2006; Slater et al., 2006). Yet, this and programs such as Brazil’s Bolsa Familia and Bolsa Verde
(UNDP, 2012) offer few concrete pathways to tackling systemic vulnerabilities and inequalities that inhibit effective responses to
severe shocks, though they stress the role of local governments in addressing long-term livelihood security and well-being in addition
to short-term disaster relief (Gilligan et al., 2009; Conway and Schipper, 2011; Béné et al., 2012; UNDP, 2012). Local governments in
urban contexts have limited capacities to address livelihood security, but more scope to increase resilience through risk-reducing
infrastructure (Satterthwaite and Mitlin, 2013).
Disaster risk reduction (DRR): The development and application of DRR (see Glossary) has been among the most important routes
for highlighting risks of extreme weather among local governments and civil society, and came to the fore as the concentration of
disaster deaths from extreme weather in LICs and MICs became evident (UNISDR, 2009, 2011). However, the accumulated effect of
several small-scale events is often more damaging than large-scale ones (Aryal, 2012). DRR is now increasingly employed as an
adaptation measure, for example, through community-based climate risk reduction (Tompkins et al., 2008; McSweeney and Coomes,
2011; Meenawat and Sovacool, 2011; IPCC, 2012b) and has helped identify DRR roles for local governments (IFRC, 2010). Yet,
sometimes disaster management-oriented adaptation can favor property and investments of the relatively richer and divert attention
and funding from measures that address disadvantaged people, as suggested in a case study of Vietnam (Buch-Hansen, 2013). The
effectiveness of DRR in supporting pro-poor climate change adaptation will depend on governance structures to address changing
risk contexts in policies and investments while responding to the needs and priorities of their low-income population. Lessons
learned from Hurricane Katrina and the Tōhoku earthquake and tsunami showcase the multiplier effect of a disaster on top of
underlying structural inequalities. Their persistence years later, as witnessed with Katrina (Schwartz, 2007; Zottarelli, 2008; Fussell et
al., 2010) further stresses the need for expanded analyses beyond disaster events themselves and the recognition of the many factors
that perpetuate the vicious cycle of poverty, multidimensional deprivation, and inequality.
Energy access: Energy is critical for rural development (Barnes et al., 2010; Kaygusuz, 2011, 2012) and for alleviation of urban
poverty (Parikh et al., 2012). One proposed climate-resilient pathway is to boost renewable energy use, which could increase energy
access for billions of people currently without access to safe and efficient energy while cutting greenhouse gas emissions from rising
non-renewable energy consumption (Casillas and Kammen, 2010; Edenhofer et al., 2011). Benefits include better health (see also
Chapter 11), employment, and cost savings relative to fossil fuels (Edenhofer et al., 2011; Jerneck and Olsson, 2012).
818
Chapter 13 Livelihoods and Poverty
13
w
here legal reforms to secure the rights of poor people exist, as in
Mexico’s Climate Law, inequalities persist (MacLennan and Perch, 2012).
Without addressing the climatic, social, and environmental stressors
that shape livelihood trajectories, including poverty traps (see Figure
13-2), and the underlying causes of poverty, persistent inequalities, and
uneven resource access and institutional support, adaption efforts and
policies will be nothing more than temporary fixes. Poverty alleviation
alone will not necessarily lead to more equality (Pogge, 2009; Milanovic,
2012). Box 13-2 provides insight into three examples.
13.4.2. Toward Climate-Resilient Development Pathways
Given the multiple challenges at the climate-poverty-development
nexus, debates increasingly focus on transforming the development
pathways themselves toward greater social and environmental
sustainability, equity, resilience, and justice, calling for a fundamental
shift toward near- and long-term climate-resilient development pathways
(see Chapter 20). This perspective acknowledges the shortcomings in
dominant global development pathways, above all rising levels of
consumption and emissions, privatization of resources, and limited
capacities of local governments and civil society to counter these trends
(Pelling, 2010; Eriksen et al., 2011; O’Brien, 2012; UN, 2012a).
At Rio+20 in 2012, an Open Working Group was created by the UN
General Assembly to develop Sustainable Development Goals (SDGs)
building on the Millennium Development Goals (MDGs), which are
criticized for not explicitly addressing the root causes of poverty,
inequality, or climate change (Melamed, 2012; UN, 2012b) and the
anticipated failure to reach MDG 1 (eradicate extreme poverty and
hunger by 2015), with or without climate change (Tubiello et al., 2008).
Early SDG debates reveal a stronger focus on eradicating extreme
poverty and environmental problems facing poor people (UN, 2012a).
This framing of development acknowledges shared global futures that
require collective action from the richest, not merely promoting welfare
for the poorest, to address both climate change and poverty (Ayers and
Dodman, 2010; UN, 2012a,b). Little information exists to date to project
how these SDGs will support climate-resilient development pathways.
Formulating goals, however, will not suffice unless the global institutional
framework for sustainable development is radically reformed (Biermann
et al., 2012).
Paying attention to dynamic livelihoods and multidimensional poverty
and the multifaceted impacts of climate change and climate change
responses is central to achieving climate-resilient development pathways
(see Chapter 20). Evidence from Sections 13.2 and 13.3 suggests that
increasing global inequality, new poverty in MICs and HICs, and
more people shifting from transient to chronic poverty overlaid with
business-as-usual development and climate policies will bring poor and
marginalized people precariously close to the two most undesirable
future scenarios as conceptualized in the shared socioeconomic pathways
(SSPs) (see Chapter 1): social fragmentation (fragmented world) and
inequality (unequal world). At the community level, inadequate governance
structures and elite capture often propel less affluent households into
deeper poverty. There is high agreement among scholars of global
governance that fragmentation also exists at the level of the global
climate regime (Biermann, 2010; Roberts, 2011; Mol, 2012), rooted in
e
ntrenched inequalities (Parks and Roberts, 2010). The extent to which
fragmentation promotes positive or negative outcomes of climate and
development goals is contested, ranging from polycentric governance
modes (Ostrom, 2010) to conflictive fragmentation (Biermann et al.,
2009; Mittelman, 2013). Evidence from this chapter suggests that, in
order to move toward the mid- and long-term SSP 1 (sustainability), a
fundamental rethinking of poverty and development will need to
emphasize equity among poor and non-poor people to collectively
address greenhouse gas emissions and vulnerabilities while striving
toward a joint, just, and desirable future.
13.5. Synthesis and Research Gaps
Previous IPCC reports have stated that climate change would cause
disproportionally adverse effects for the world’s poor people. However,
they presented a rather generalized view that all poor people were
vulnerable, in contrast to earlier scientific studies highlighting vulnerability
as contextual with variation over time and space. This chapter is devoted
to exploring poverty in relation to climate change, a new theme in the
IPCC. It uses a livelihood lens to assess the interactions between climate
change and the multiple dimensions of poverty, not just income poverty.
This lens also reveals how inequalities perpetuate poverty, and how
they shape differential vulnerabilities and in turn the differentiated
impacts of climate change on individuals and societies. This chapter
illustrates that climate change adds an additional burden to poor people
and their livelihoods, acting as a threat multiplier. Moreover, it emphasizes
that climate change may create new groups of poor people, not only in
low-income countries but also in middle- and high-income countries.
Neither alleviating poverty nor decreasing vulnerabilities to climate
change can be achieved unless entrenched inequalities are reduced.
This chapter concludes that climate change policy responses reviewed
in this chapter often do not benefit poor people, and highlights lessons
for climate-resilient development pathways.
Eight major research gaps are identified with respect to the observed
and projected impacts of climate change and climate change responses:
• Poverty dynamics are not sufficiently accounted for in current
climate change research. Most research as well as poverty
measurements remain focused on only one or two dimensions of
poverty. Insufficient work assesses the distribution of poverty at the
level of households, spatial and temporal shifts, critical thresholds
that plunge some transient poor into chronic poverty, and poverty
traps, in the context of climatic and non-climatic stressors. Many
of these dynamics remain hidden, incompletely captured in poverty
statistics and disaster and development discourses. Key assumptions
in many economic models (e.g., constant within-country distribution
of per capita income over time, linear relationship between economic
growth and poverty headcounts) are ill suited to capture local and
subnational poverty dynamics, confounding projections of future
poverty levels.
• Though an abundance of studies exists that explore climate change
impacts on livelihoods, the majority does not focus on continuous
struggles and trajectories but only offers snapshots. An explicit
analysis of livelihood dynamics would more clearly reveal how
people respond to a series of climatic stressors and shocks over
time.
819
Livelihoods and Poverty Chapter 13
13
•
Few studies examine how structural inequalities, power imbalances,
and intersecting axes of privilege and marginalization shape
differential vulnerabilities to climate change. Although there is
growing literature on climate change and gender as well as on
indigeneity, other axes such as age, class, race, caste, and (dis)ability,
remain underexplored. Understanding how simultaneous and
intersecting inequalities determine climate change impacts shows
which particular drivers of vulnerability are at play in one context,
while absent in another.
• Very limited research examines climate change impacts on poor
people and livelihoods in middle- to high-income countries. Despite
mounting evidence of observed impacts of climatic events on the
poor in MICs and HICs, as documented for the European heat wave,
Hurricane Katrina in the USA, and the 10-year drought in Australia,
the majority of research on the poverty-climate nexus remains
focused on the poorest countries.
• There remains a lack of rigorous data collection and analysis
regarding small-scale disasters, that is, those that go unnoticed
because of their limited extent, but whose accumulated effect
may exceed large-scale disasters. This gap leads to significant
underestimation of lived experiences with climate change, in which
particular loss and harm remain largely undetected. There is a need
for more climatology research informed by the needs of poor people
and vulnerable livelihoods, for instance on the effects of changing
winds as a combined result of climate and land cover change, and
their effects on increasing evaporation and water availability.
• Not enough consideration is given to extreme stressors and shocks,
for example, under potential global mean warming of +4°C and
beyond, underestimating impacts on poor and marginalized people
and limits to adaptation.
• There is a lack of in-depth research on the direct and indirect effects
of mitigation and adaptation climate-related policies such as CDM,
REDD+, biofuels, and insurance on livelihoods, poverty, and inequality.
More in-depth research has the potential to improve the capacity
of these policies to benefit poor people.
• Limited understanding exists of how poverty alleviation and more
equality between the poor and the non-poor are best built into
climate-resilient development pathways to strive toward a just and
desirable future for all.
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