613

Rural Areas

Coordinating Lead Authors:

Purnamita Dasgupta (India), John F. Morton (UK)

Lead Authors:

David Dodman (Jamaica), Barış Karapinar (Turkey/Switzerland), Francisco Meza (Chile),

Marta G. Rivera-Ferre (Spain), Aissa Toure Sarr (Senegal), Katharine E. Vincent (South Africa)

Contributing Authors:

Ashish Aggarwal (India), Netra Chhetri (USA/Nepal), Tracy Cull (South Africa),

Jose Gustavo Feres (Brazil), Jeremy Haggar (UK), George Hutchinson (UK), Feliu López-i-Gelats

(Spain), Megan Mills-Novoa (USA), Nandan Nawn (India), Catherine Norman (USA),

Andreas Scheba (Austria), Tetsuji Tanaka (Japan)

Review Editors:

Edward R. Carr (USA), Nirivololona Raholijao (Madagascar)

Volunteer Chapter Scientist:

Hauke Broecker (Germany)

This chapter should be cited as:

Dasgupta

, P., J.F. Morton, D. Dodman, B. Karapinar, F. Meza, M.G. Rivera-Ferre, A. Toure Sarr, and K.E. Vincent,

2014: Rural areas. 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. 613-657.

614

Executive Summary............................................................................................................................................................ 616

9.1. Introduction ............................................................................................................................................................ 618

9.1.1. Rationale for the Chapter ................................................................................................................................................................. 618

9.1.2. Deﬁnitions of the Rural ..................................................................................................................................................................... 618

9.2. Findings of Recent Assessments ............................................................................................................................. 619

9.3. Assessing Impacts, Vulnerabilities, and Risks ......................................................................................................... 619

9.3.1. Current and Future Economic, Social, and Land Use Trends in Rural Areas ....................................................................................... 619

9.3.2. Observed Impacts ............................................................................................................................................................................. 619

9.3.3. Future Impacts .................................................................................................................................................................................. 623

9.3.3.1.Economic Base and Livelihoods ........................................................................................................................................... 623

Box 9-1. Impacts of Climate Change on Tropical Beverage Crops ................................................................................... 626

9.3.3.2.Infrastructure ....................................................................................................................................................................... 628

9.3.3.3.Spatial and Regional Interconnections ................................................................................................................................. 628

9.3.3.4.Second-Order Impacts of Climate Policy ............................................................................................................................... 629

9.3.4. Valuation of Climate Impacts ........................................................................................................................................................... 630

9.3.4.1.Agriculture ........................................................................................................................................................................... 631

9.3.4.2.Other Rural Sectors: Water, Fisheries, Livestock, Mining ....................................................................................................... 632

9.3.4.3.Extreme Weather Events, Sea Level Rise .............................................................................................................................. 633

9.3.4.4.Recreation and Tourism; Forestry .......................................................................................................................................... 633

9.3.5. Key Vulnerabilities and Risks ............................................................................................................................................................ 633

9.3.5.1.Drivers of Vulnerability and Risk ........................................................................................................................................... 633

9.3.5.2.Outcomes ............................................................................................................................................................................. 635

Box 9-2. Tourism and Rural Areas .................................................................................................................................... 636

9.4. Adaptation and Managing Risks ............................................................................................................................. 637

9.4.1. Framing Adaptation .......................................................................................................................................................................... 637

9.4.2. Decision Making for Adaptation ....................................................................................................................................................... 638

9.4.3. Practical Experiences of Adaptation in Rural Areas ........................................................................................................................... 638

9.4.3.1.Agriculture ........................................................................................................................................................................... 638

9.4.3.2.Water ................................................................................................................................................................................... 638

9.4.3.3.Forestry and Biodiversity ...................................................................................................................................................... 640

Box 9-3. Adaptation Initiatives in the Beverage Crop Sector .......................................................................................... 641

9.4.3.4.Fisheries ............................................................................................................................................................................... 642

9.4.4. Limits and Constraints to Rural Adaptation ...................................................................................................................................... 642

Box 9-4. Factors Inﬂuencing Uptake and Utility of Climate Forecasts in Rural Africa ................................................................ 643

Table of Contents

615

Rural Areas Chapter 9

9.5. Key Conclusions and Research Gaps ....................................................................................................................... 643

9.5.1. Key Conclusions ................................................................................................................................................................................ 643

9.5.2. Research Gaps .................................................................................................................................................................................. 645

References ......................................................................................................................................................................... 645

Frequently Asked Questions

9.1: What is distinctive about rural areas in the context of climate change impacts, vulnerability, and adaptation? .............................. 618

9.2: What will be the major climate change impacts in rural areas across the world? ............................................................................ 630

9.3: What will be the major ways in which rural people adapt to climate change? ................................................................................. 642

616

Chapter 9 Rural Areas

Executive Summary

Rural areas still account for almost half the world’s population, and about 70% of the developing world’s poor people. {9.1.1}

There is a lack of clear definition of what constitutes rural areas, and definitions that do exist depend on definitions of the urban. {9.1.2} Across

the world, the importance of peri-urban areas and new forms of rural-urban interactions are increasing (limited evidence, high agreement).

{9.1.3} Rural areas, viewed as a dynamic, spatial category, remain important for assessing the impacts of climate change and the prospects for

adaptation. {9.1.1}

Climate change in rural areas will take place in the context of many important economic, social, and land-use trends (very high

confidence). In different regions, absolute rural populations have peaked or will peak in the next few decades. {9.3.1} The proportion of the

rural population depending on agriculture is extremely varied across regions, but declining everywhere. Poverty rates in rural areas are higher

than overall poverty rates, but also falling more sharply, and the proportions of population in extreme poverty accounted for by rural people

are also falling: in both cases with the exception of sub-Saharan Africa, where these rates are rising. {Figure 9-2} Accelerating globalization,

through migration, labor linkages, regional and international trade, and new information and communication technologies, is bringing about

economic transformation in rural areas of both developing and developed countries. {9.3.1}

Rural people in developing countries are subject to multiple non-climate stressors, including under-investment in agriculture

(though there are signs this is improving), problems with land and natural resource policy, and processes of environmental

degradation (very high confidence).

In developing countries, the levels and distribution of rural poverty are affected in complex and

interacting ways by processes of commercialization and diversification, food policies, and policies on land tenure. In developed countries, there

are important shifts toward multiple uses of rural areas, especially leisure uses, and new rural policies based on the collaboration of multiple

stakeholders, the targeting of multiple sectors, and a change from subsidy-based to investment-based policy. {9.3.1, Table 9-3}

Impacts of climate change on the rural economic base and livelihoods, land use, and regional interconnections are at the latter

stages of complex causal chains (high confidence). These flow through changing patterns of extreme events and/or effects of climate

change on biophysical processes in agriculture and less-managed ecosystems. {9.3.3} This increases both the uncertainty associated with

detection and attribution of current impacts {9.3.2}, and with projections of specific future impacts. {9.3.3}

Structural features of farm households and communities affect their vulnerability to climate change in complex ways (high

confidence).

There is low agreement on some of the key factors associated with vulnerability or resilience in rural areas {9.3.5.1}, including

rainfed as opposed to irrigated agriculture {9.3.5.1.1}, small-scale and family-managed farms, and integration into world markets. {9.3.5.1.2}

There is high agreement on the importance for resilience of access to land and natural resources, flexible local institutions {9.3.5.1.3}, and

knowledge and information {9.3.5.1.6}, and on the association of gender inequalities with vulnerability. {9.3.5.1.5} Specific livelihood niches

such as pastoralism, mountain farming systems, and artisanal fisheries are vulnerable and at high risk of adverse impacts (high confidence),

partly owing to neglect, misunderstanding, or inappropriate policy toward them on the part of governments. {9.3.5.2}

Cases in the literature of observed impacts on rural areas often suffer from methodological problems of attribution, but evidence

for observed impacts, both of extreme events and other categories, is increasing (medium confidence).

Impacts attributable to climate

change include some direct impacts of droughts, storms, and other extreme events on infrastructure and health (low confidence globally, but

medium confidence in certain regions), as well as longer-term declining yields of major crops, from which impacts on income and livelihoods

can be inferred with low confidence. There is high confidence in geographically specific impacts, such as glacier melt in the Andes. {9.3.2}

Major impacts of climate change in rural areas will be felt through impacts on water supply, food security {9.3.3.1}, and

agricultural incomes {9.3.4.1} (high confidence).

Shifts in agricultural production, of food and non-food crops, are projected for many

areas of the world (high confidence). {9.3.3.1} Price rises, which may be induced by climate shocks as well as other factors {9.3.3.3.2}, have a

disproportionate impact on the welfare of the poor in rural areas, such as female headed households and those with limited access to modern

agricultural inputs, infrastructure, and education. {9.3.3.1} The time scale for impacts varies across regions and sectors, and by the nature of the

specific climatic impact.

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Rural Areas Chapter 9

Climate change will impact international trade volumes in both physical and value terms (limited evidence, medium agreement).

Importing food can help countries adjust to climate change-induced domestic productivity shocks while short-term food deficits in low-income

countries may have to be met through food aid. Options exist for adaptations within international agricultural trade (medium confidence).

Deepening agricultural markets and improving the predictability and the reliability of the world trading system through trade reform, as well as

investing in additional supply capacity of small-scale farms in developing countries, could result in reduced market volatility and manage food

supply shortages caused by climate change. {9.3.3.3.2}

Migration patterns will be driven by multiple factors of which climate change is only one (high confidence). {9.3.3.3.1} Given

these multiple drivers of migration (economic, social, political, demographic, and environmental) and the complex interactions that mediate

migratory decision making by individuals or households, establishment of a relation between climate change and intra-rural and rural-to-urban

migration, observed or projected, remains a major challenge.

Climate policies, such as increasing energy supply from renewable resources, encouraging cultivation of biofuels, or payments

under Reducing Emissions from Deforestation and Forest Degradation (REDD), will have significant secondary impacts, both

positive (increasing employment opportunities) and negative (landscape changes, increasing conflicts for scarce resources), in

some rural areas (medium confidence). {9.3.3.4}

There is a need to understand how implementation of these policies will impact on rural

livelihoods. These secondary impacts, and trade-offs between mitigation and adaptation in rural areas, have implications for governance,

including the need to promote participation of rural stakeholders.

Most studies using valuation methodologies conclude that climate change impacts will be substantial, especially for developing

countries, owing to their economic dependence on agriculture and natural resources, low adaptive capacities, and geographical

locations (very high confidence). {9.3.4}

Valuation of climate impacts needs to draw on both monetary and non-monetary indicators. The

valuation of non-marketed ecosystem services {9.3.4.5} and the limitations of economic valuation models that aggregate across multiple

contexts {9.3.4} pose challenges for valuing impacts in rural areas (high confidence).

There is a growing body of literature on adaptation practices in both developed and developing country rural areas {9.4.1},

including documentation of practical experience in agriculture, water, forestry and biodiversity, and, to a lesser extent, fisheries

{9.4.3} (very high confidence).

Public policies supporting decision making for adaptation exist in developed and, increasingly, in developing

countries, and there are also examples of private adaptations led by individuals, companies, and non-governmental organizations (high

confidence). {9.4.2} Constraints on adaptation come from lack of access to credit, land, water, technology, markets, knowledge and information,

and perceptions of the need to change; and are particularly pronounced in developing countries (high confidence). {9.4.4} Gender and

institutions affect access to adaptation options and the presence of barriers to adaptation (very high confidence). {9.4.4}

618

Chapter 9 Rural Areas

9.1. Introduction

.1.1. Rationale for the Chapter

his chapter assesses the impacts of climate change on, and the prospects

for adaptation in, rural areas. Rural areas include diverse patterns of

settlement, infrastructure, and livelihoods, and relate in complex ways

with urban areas. The chapter shows that rural areas experience specific

vulnerabilities to climate change, both through their dependence on

natural resources and weather-dependent activities and their relative

lack of access to information, decision making, investment, and services.

Adaptation strategies will need to address these vulnerabilities. Some of

the key starting points, which affect the scope and coverage of literature

assessed in this chapter, are as follows:

• Rural areas, even after significant demographic shifts, still account

for 3.3 billion people, or almost half (47.9%) of the world’s total

population (UN DESA Population Division, 2013).

• The overwhelming majority of the world’s rural population (3.1 billion

people, or 91.7% of the world’s rural population, or 44.0% of the

world’s total population) live in less developed or least developed

countries (UN DESA Population Division, 2013).

• Rural dwellers also account for about 70% of the developing

world’s poor people. IFAD (2010) states that around 70% of the

extreme poor in developing countries lived in rural areas in 2005.

Ravallion et al. (2007), using 2002 data and poverty lines of US$1.08

or US$2.15, in each case with urban poverty lines adjusted upward

to recognize additional non-food spending, give a figure of around

75% of people, under either poverty line, being rural.

•

Rural areas are a spatial category, associated with certain patterns

of human activity, but with those associations being subject to

continuous change.

• Rural areas are largely defined in contradistinction to urban areas,

but that distinction is increasingly seen as problematic.

• Rural populations have, and will have, a variety of income sources

and occupations, within which agriculture and the exploitation of

natural resources have privileged, but not necessarily predominant,

positions.

The chapter will complement the treatment of issues also dealt with in

Chapters 4 and 7, but will primarily look at how biophysical impacts of

climate change on agriculture and on less-managed ecosystems translate

into impacts on human systems, and in this regard will complement

sections of Chapters 12 and 13 and other sectoral and regional chapters.

The important impacts of climate change on human health are covered

in Chapter 11. In accordance with the proportion of the rural population

found in developing countries, literature on these countries is given

prominence, but issues of impact, vulnerability, and adaptation in

developed countries are also assessed.

9.1.2. Definitions of the Rural

“Rural” refers generally to areas of open country and small settlements,

but the definition of “rural areas” in both policy-oriented and scholarly

literature are terms often taken for granted or left undefined, in a

process of definition that is often fraught with difficulties (IFAD, 2010).

Frequently Asked Questions

FAQ 9.1 | What is distinctive about rural areas in the context of climate change

impacts, vulnerability, and adaptation?

Nearly half of the world’s population, approximately 3.3 billion people, lives in rural areas, and 90% of those people

live in developing countries. Rural areas in developing countries are characterized by a dependence on agriculture

and natural resources; high prevalence of poverty, isolation, and marginality; neglect by policymakers; and lower

human development. These features are also present to a lesser degree in rural areas of developed countries, where

there are also closer interdependencies between rural and urban areas (such as commuting), and where there are

also newer forms of land use such as tourism and recreational activities (although these also generally depend on

natural resources).

The distinctive characteristics of rural areas make them uniquely vulnerable to the impacts of climate change because:

• Greater dependence on agriculture and natural resources makes them highly sensitive to climate variability,

extreme climate events, and climate change.

• Existing vulnerabilities caused by poverty, lower levels of education, isolation, and neglect by policymakers can

all aggravate climate change impacts in many ways.

Conversely, rural people in many parts of the world have, over long time scales, adapted to climate variability, or

at least learned to cope with it. They have done so through farming practices and use of wild natural resources

(often referred to as indigenous knowledge or by similar terms), as well as through diversiﬁcation of livelihoods and

through informal institutions for risk-sharing and risk management. Similar adaptations and coping strategies can,

given supportive policies and institutions, form the basis for adaptation to climate change, although the effectiveness

of such approaches will depend on the severity and speed of climate change impacts.

619

Rural Areas Chapter 9

Ultimately, in developing countries as well as developed countries, the

rural is defined as the inverse or the residual of the urban (Lerner and

Eakin, 2010). Human settlements in fact exist along a continuum from

“rural” to “urban,” with “large villages,” “small towns,” and “small

urban centers” not clearly fitting into one or the other. The variations in

definitions from country to country can best be described through

several examples (from both developed and developing countries of

different sizes) shown in Table 9-1.

Researchers have increasingly recognized that the simple dichotomy

between “rural” and “urban” is extremely problematic (Simon et al.,

2006, p. 4). Additional categories such as “peri-urban areas” (Webster

2002; Bowyer-Bower, 2006; Simon et al., 2006; Simon, 2008; Lerner and

Eakin, 2010) and “desakota” (McGee, 1991; Desakota Study Team, 2008;

Moench and Gyawali, 2008) allow more nuanced analysis of the permeable

boundaries of rural and urban areas and the diversified economic systems

that exist across the urban-rural spectrum; see Box CC-UR.

While remaining aware of issues of definition, this chapter in general

assesses the literature on rural areas using whatever definitions of the

rural are used in that literature. Global statistics collated by international

organizations and cited here are generally aggregations of national

statistics compiled under each national definition.

9.2. Findings of Recent Assessments

The Fourth Assessment Report (AR4) of the IPCC contains no specific

chapter on “rural areas.” Material on rural areas and rural people is

found throughout the AR4, but rural areas are approached from specific

viewpoints and through specific disciplines. Table 9-2 summarizes key

findings on rural areas from AR4 (particularly Easterling et al. (2007) on

agriculture; Wilbanks et al. (2007) on industry, settlement, and society;

and Klein et al. (2007) on links between adaptation and mitigation),

and relevant findings from the International Assessment of Agricultural

Knowledge, Science and Technology for Development (McIntyre et al.,

2009). All of these sources stress uncertainty, the importance of

non-climate trends, complexity, and context-specificity in any findings

on rural areas and climate change.

9.3. Assessing Impacts,

Vulnerabilities, and Risks

9.3.1. Current and Future Economic, Social,

and Land Use Trends in Rural Areas

Climate change in rural areas will take place against the background

of the trends in demography, economics, and governance that are

shaping those areas. While there are major points of contact between

the important trends in developing and developed countries, and the

analytical approaches used to discuss them, it is easier to discuss trends

separately for the two groups of countries. In particular there is a close

association in developing countries between rural areas and poverty.

Table 9-3 summarizes and compares the most important trends across

the two groups of countries. Figures 9-1 and 9-2 and Table 9-4 focus on

two specific trends in developing countries: demographic trends and

trends in poverty indicators.

9.3.2. Observed Impacts

Documentation of observed impacts of climate change on rural areas

involves major questions of detection and attribution (see Chapter 18).

Whilst having potential, there are complications with using traditional

knowledge and farmer perceptions to detect climate trends (Rao et al.,

2011; see also Box 18-4). Implied equivalence between local perceptions

of climate change, local decadal trends, extreme events, and global

change is common, and often used without systematic discussion of

the challenges (Paavola, 2008; Ensor and Berger, 2009; Castro et al.,

2012). This is not a problem in the context of detailed social-scientific

analysis of vulnerability, adaptive capacity, and their determinants, but

becomes more problematic to use as evidence for observed impact.

Detection and attribution of extreme events to climate change is no

Country Term Deﬁ nition Reference

Australia

ajor urban area Population of more than 100,000 Australian Bureau of Statistics (2013)

Other urban area Population of 1000–99,999

ural area Includes small towns with a population of 200–999

China

Major urban area Population of more than 10,000 Ministry of Construction (1993)

edium urban area Population of 3000–9999

Small urban area Population of fewer than 3000

ajor village Population of 1000–3000

Medium village Population of 300–1000

Small village Population of fewer than 300

India

rban area Population of 5000 or more; or where at least 75% of the male working population is non-agricultural; or

having a density of population of at least 400 people km

–2

. It is implied that all non-urban areas are rural.

overnment of India (2012)

Jamaica

rban place Population of more than 2000 people; and provision of a certain set of amenities and facilities that are

deemed to indicate “modern living”. It is implied that all non-urban areas are rural.

tatistical Institute of Jamaica (2012:iv)

United States

of America

ural area All territory outside of deﬁ ned urbanized areas and urban clusters, that is, open country and settlements

with fewer than 2500 residents; with population densities as high as 386 people km

–2

omach (2005)

Table 9-1 | Indicative examples of deﬁ nitions of the “rural” and the “urban” in selected countries.

620

Chapter 9 Rural Areas

ess challenging (Seneviratne et al., 2012). Exposure to non-climate

trends and shocks further complicates the issue (Nielsen and Reenberg,

2010; see also Section 3.2.7).

The impacts of climate change on patterns of settlement, livelihoods,

and incomes in rural areas will be the result of multi-step causal chains

of impact. Typically, those chains will be of two sorts. One sort will

involve extreme events, such as floods and storms, as they impact on

rural infrastructure and cause direct loss of life. The other sort will

involve impacts on agriculture or on ecosystems on which rural people

depend. These impacts may themselves stem from extreme events, from

changing patterns of extremes due to climate change, or from changes

in mean conditions. The detection and attribution of extreme events is

discussed by the IPCC Special Report on Managing the Risks of Extreme

Events and Disasters to Advance Climate Change Adaptation (Seneviratne

et al., 2012). The detection and attribution of impacts on ecosystems

and on agriculture are dealt with in Chapters 4 and 7 of this report. Both

exercises are complex.

eneviratne et al. (2012) give a detailed and critical assessment of the

detection and attribution of observed patterns of extreme events, which

shows greatly varying levels of confidence in the attribution to climate

change of global and regional trends, and that “attribution of single

extreme events to anthropogenic climate change is challenging” (p. 112).

They state that it is likely there has been a worldwide increase in

extreme high-water events during the late 20th century, with a likely

anthropogenic influence on it. They have medium confidence in detecting

trends toward more intense and frequent droughts in some parts of the

world (southern Europe and West Africa) since 1950. They note that

opposite trends exist elsewhere, and that there is low confidence in any

trend in drought in, for example, East Africa. WG I AR5 Chapter 2 similarly

ascribes low confidence in a global observed trend in drought in the

later 20th century, with a likely increase in frequency and intensity of

drought in the Mediterranean and West Africa and a likely decrease in

central North America. Lyon and DeWitt (2012) see a “recent and abrupt

decline in the East African long rains” since 1999. Seneviratne et al.

(2012) assign low confidence to any observed long-term increases in

Finding Source

Importance of

non-climate

trends

The signiﬁ cance of climate change needs to be considered in the multi-causal context of its interactions with other non-climate sources of change

nd stress (e.g., water scarcity, governance structures, institutional and jurisdictional fragmentation, limited revenue streams for public sector roles,

resource constraints, or inﬂ exible land use patterns).

W 7.4.2

6.7.5

Different development paths may increase or decrease vulnerabilities to climate-change impacts. W 7.7

Neglect by policymakers and underinvestment in infrastructure and services has negatively affected rural areas. I 1.3.4

Policy neglect speciﬁ cally disfavors rural women. I 1.3.4

Assessment of climate change impacts on agriculture has to be undertaken against a background of demographic and economic trends in rural areas. E 5.3.2

Global numbers of people at risk from hunger will be affected by climate change, but more by socioeconomic trends as captured in the difference

between the SRES scenarios.

E 5.6.5

Speciﬁ c

characteristics

of smallholder

agriculture

Subsistence and smallholder livelihood systems suffer from a number of non-climate stressors, but are also characterized by having certain resilience

factors (efﬁ ciencies associated with the use of family labor, livelihood diversity to spread risks).

E 5.3.2

Traditional knowledge of agriculture and natural resources is an important resilience factor. I 2.1.2, 3.2.2, 3.2.3

E 5.3.2

CC4

The combination of stressors and resilience factors gives rise to complex and locally speciﬁ c impacts, resistant to modeling. E 5.4.7

W 7.2, 7.4, 7.5

Impacts on

agriculture

and

agricultural

trade

In low-latitude regions, temperature increases of 1–2°C are likely to have negative impacts on yields of major cereals. Further warming has

increasingly negative impacts in all regions.

E 5.4.2

Increases in global mean temperatures (GMTs) of 2–3°C might lead to a small rise or decline (10–15%) in food (cereals) prices, while GMT increases

in the range of 5.5°C or more might result in an increase in food prices of, on average, 30%.

E 5.6.1

Forestry

Loss of forest resources through climate change may affect 1.2 billion poor and forest-dependent people, including through impacts on non-timber

forest products.

E 5.4.5

Valuation

Robust valuation of climate change impact on human settlements is difﬁ cult, and social and environmental costs are poorly captured by monetary

metrics: non-monetary valuation methods should be explored.

W 7.4.3, 7.5

I 8.2.5

Adaptation

The need and the capacity to adapt vary considerably from region to region, and from farmer to farmer. I 1.3.3

Adaptation actions can be effective in achieving their speciﬁ c goals, but they may have other (positive or negative) effects, including resource

competition.

I 6.7.5

Diversiﬁ cation of agricultural and non-agricultural livelihood strategies is an important adaptation trend, but requires institutional support and access

to resources.

E 5.5.1, 5.5.2

The effectiveness of adaptation efforts is likely to vary signiﬁ cantly between and within regions, depending on geographic location, vulnerability to

current climate extremes, level of economic diversiﬁ cation and wealth, and institutional capacity.

I 6.8

Multi-stakeholder processes are increasingly important with respect to climate change adaptation. I 7.5.3

Links between

adaptation

and mitigation

Mitigation and adaptation policies are in many cases, and certainly for agriculture, closely linked. K 18.4.3, 18.7.1

E 5.4.1, 5.4.2, 5.6.5

W 7.1, 7.7

Table 9-2 | Relevant ﬁ ndings on rural areas from the IPCC Fourth Assessment Report and the International Assessment of Agricultural Science and Technology for Development.

Sources: W = Wilbanks et al. (2007); E = Easterling et al. (2007); I = McIntyre et al. (2009); K = Klein et al. (2007); CC4 = Cross-Chapter Case Study C4 “Indigenous knowledge

for adaptation to climate change” in AR4 (Parry et al., 2007).

621

Rural Areas Chapter 9

ropical cyclone activity, as does WGI AR5 Chapter 2, and to attribution

of any changes in cyclone activity to anthropogenic influence. WGI

AR5 Chapter 2 states that an observed increase in the frequency and

intensity of North Atlantic cyclones is virtually certain. It also describes

varying regional trends toward heavy precipitation events, very likely

in central North America. Section 3.2.7 ascribes medium confidence

to observed increased likelihood of flooding at the scale of some

regions.

Handmer et al. (2012) discuss both observed and projected impacts of

extreme events on human systems and ecosystems, with numerous

examples of diverse, widespread negative impacts (see also Chapter

18). Important categories of extreme events causing negative impacts

in rural areas include tropical storms and droughts: Hurricane Stan in

October 2005 affected nearly 600,000 people on the Chiapas coast as

a consequence of flooding and sudden river overflows (Saldaña-Zorrilla,

2008). Droughts in rural areas produce severe economic stresses,

including employment reduction and migration (Gray and Mueller,

012). Agricultural livelihoods are affected by droughts. Ericksen et al.

(2012) review a variety of livestock mortality rates for recent droughts

in the Horn of Africa, ranging up to 80% of livestock in southern Kenya

in 2009.

Climate change impacts on agriculture and ecosystems run through rising

temperature and changes in rainfall variability and seasonality as well

as through extreme events. Changes in temperature caused reduction

in global yields of maize and wheat by 3.8 and 5.5% respectively from

1980 to 2008 relative to a counterfactual without climate change, which

offset in some countries some of the gains from improved agricultural

technology (Lobell et al., 2011; see also Section 7.2.1.1). Badjeck et al.

(2010) discuss current and future impacts on fisherfolk across the world.

Many local-level studies are subject to the attribution problems mentioned

above, but Wellard et al. (2012) cautiously note a convergence of climate

data with the perceptions of farmers and officials to the effect that over

the last 30 years the rainfall in Malawi has become less predictable, that

the rainy season is arriving later in the year causing delays in planting

Developed countries Developing countries

Demographic

trends

Rural population accounts for 22.3% of the total population (or about 276 million

eople) (UN-DESA Population Division, 2012). Rural areas account for 75% of land

area in OECD countries (OECD, 2006).

Rural population has peaked (absolute numbers) in Europe and North America.

Rural depopulation in some places, but also counter-urbanization with people

moving from urban to rural areas elsewhere.

Rural population accounts for 50.3% of the total population (or about 2.5 billion

eople) in less developed countries (excluding LDCs), 71.5% (or about 608 million

people) in LDCs.

Rural population has already peaked in Latin America and the Caribbean, East and

Southeast Asia; expected to peak around 2025 in the Middle East, North Africa,

South and Central Asia; around 2045 in sub-Saharan Africa.

Dependence on

agriculture

Agriculture accounts for only 13% of rural employment in the EU (OECD, 2006),

and less than 10% on average across developed countries; however, it has a strong

indirect inﬂ uence on rural economies.

Increased competition as a result of economic globalization has resulted in

agriculture no longer being the main pillar of the rural economy in Europe.

Economic policies are primary drivers, with social re-composition and economic

restructuring taking place (Marsden, 1999; Lopez-i-Gelats et al., 2009).

Proportion of rural population engaged in agriculture declining in all regions

(Figure 9-2). Agriculture still provides jobs for 1.3 billion smallholders and landless

workers (World Bank, 2008).

Non-agricultural including labor-based and migration-based livelihoods increasingly

existing alongside (and complementing) farm-based livelihoods. Agricultural

initiatives and growth still important for adaptation and for smallholders in Africa

and Asia (Collier et al., 2008; Osbahr et al., 2008; Kotir, 2011).

Poverty and

inequality

Per capita gross domestic product (GDP) in rural areas of OECD countries is only

83% of national average (but signiﬁ cant variation within and between countries):

driven by out-migration, aging, lower educational attainment, lower productivity of

labor, low levels of public services (OECD, 2006).

Rates of poverty (percentage of population living on less than US$2 per day) and

extreme poverty (percentage of population living on less than US$1.25 per day)

falling in rural areas in most parts of the world; but rural poverty and rural extreme

poverty rising in sub-Saharan Africa. Recent price hikes and volatility exacerbated

hunger and malnutrition among rural households, many of which are net food-

buyers (FAOSTATS, 2013). Hunger and malnutrition prevalent among rural children

in South Asia and sub-Saharan Africa (World Bank, 2007; IFAD, 2010); see Figure

9-2 and Table 9-4.

Economic,

policy,

governance

trends

Shift from agricultural (production) to leisure (consumption) activities; focus on

broader amenity values of rural landscapes for recreation, tourism, forests, and

ecosystem services (OECD, 2006; Rounsevell et al., 2006; Bunce, 2008).

Agricultural subsidies under pressure from international trade negotiations and

domestic budgetary constraints. As a result of recent price hikes, domestic price

support has been lowered in OECD countries.

New policy approach in OECD countries that focuses on investments and targets a

range of rural economic sectors and environmental services.

Interconnectedness and economic openness in rural areas have encouraged shifts

to commercial agriculture, livelihoods diversiﬁ cation and help knowledge transfers

(Section 9.3.3).

Interlinkages between land tenure, food security, and biofuel policies impact rural

poverty (see Sections 7.1 and 7.2.2 for further details).

Decentralization of governance and emergence of rural civil society. Movements

toward land reform in some parts of Asia (Kumar, 2010). Emergence of economies

in transition, characterized in places by coexistence of leading and lagging regions;

political and democratic decentralization leading to increasing complexity of policy

(World Bank, 2007).

Environmental

degradation

Different socioeconomic scenarios have varying impacts on land use and

agricultural biodiversity (Reidsma et al., 2006).

Resource degradation, environmentally fragile lands subject to overuse and

population pressures, exacerbating social and environmental challenges. Multiple

stressors increase risk, reduce resilience, and exacerbate vulnerability among rural

communities from extreme events and climate change impacts (Section 13.2.6).

Rural-urban

linkages and

transformations

Changes in land use and land cover patterns at urban-rural fringe affected by new

residential development, local government planning decisions, and environmental

regulations (Brown, D.G. et al., 2008).

Stronger rural-urban linkages through migration, commuting, transfer of public

and private remittances, regional and international trade, inﬂ ow of investment,

and diffusion of knowledge (through new information and communication

technologies) (IFAD, 2010). Continued out-migration to urban areas by the

semiskilled and low-skilled, reducing the size of the rural workforce (IFAD, 2010).

Trend for migration to small and medium-sized towns (Sall et al., 2010).

Increased volumes of agricultural trade, growing by 5% on average (annually)

between 2000 and 2008 (WTO, 2009). New initiatives of foreign direct investment

(FDI) in agriculture in the form of large-scale land acquisitions in developing

countries (World Bank, 2010; Anseeuw et al., 2012).

Table 9-3 | Major demographic, poverty-related, economic, governance, and environmental trends in rural areas of developed and developing countries.

622

Chapter 9 Rural Areas

1940 1960 1980 2000 2020 2040 20601940 1960 1980 2000 2020 2040 2060

1940 1960 1980 2000 2020 2040 2060

1940 1960 1980 2000 2020 2040 20601940 1960 1980 2000 2020 2040 2060

(e) Africa

(b) Europe

(a) Northern America

(f) Oceania

1000

2000

3000

4000

5000

6000

200

400

600

800

500

1000

1500

2000

2500

200

400

600

800

100

200

300

400

500

Population (Millions)Population (Millions)

(a)

(d)

(b)

(c)

(e)

(f)

(d) Latin America and

the Caribbean

Total population

Urban population

Rural population

Observed

Projected

Figure 9-1 | Trends in rural, urban, and total populations by region; solid lines represent observed values and dotted lines represent projections (UN DESA Population Division, 2013). Note: Regions used in the source do not correspond

with the IPCC regions covered in Chapters 22–30.

623

Rural Areas Chapter 9

of the main crops, and that damaging dry spells during the rainy season

have become more frequent.

Glacial retreat in Latin America is one of the best evidenced current

impacts on rural areas (see Section 27.3.1.1). In highland Peru there

have been rapid observed declines since 1962 in glacier area and dry-

season stream flow, on which local livelihoods depend, which accord

well with local perceptions of changes that are necessitating adaptation

(Orlove, 2009). Other studies of the area focus both on observed changes

in water availability and on glacial lake outburst floods, which are

attributable to climate change (Carey, 2010; Bury et al., 2011; Carey et

al., 2012). There is also a rich specialized literature on the impacts of

shrinking sea ice and changing seasonal patterns of ice formation and

melt on indigenous peoples in the Arctic (Ford, 2009; Beaumier and

Ford, 2010; see also Section 28.2.5.1.7).

Migration associated with weather-related extremes or longer-term

climate trends is discussed in Table 12-3, with empirical examples of

migrations linked to droughts, coastal storms, floods, and sea level rise.

The Asian Development Bank (ADB, 2012) gives a figure of 42 million

people displaced by extreme weather events in Asia and the Pacific over

2010–2011. Attribution of migration to climate change is extremely

complex, as recognized by Black et al. (2011a), because life in rural

areas across the world typically involves complex patterns of rural-

urban and rural-rural migration, subject to economic, political, social,

and demographic drivers, patterns that are modified or exacerbated by

climate events and trends rather than solely caused by them (see also

Section 12.4.1).

9.3.3. Future Impacts

This section examines the major impacts of climate change identified

or projected for rural areas, under the headings of economic base and

livelihoods; infrastructure; spatial and regional interconnections, including

migration, trade, investment, and knowledge; and second-order impacts

of climate policy. Section 9.3.4 assesses the literature on impact through

a different and specific lens, that of economic valuation. The biophysical

impacts of climate change on food crops are dealt with primarily in

Chapter 7; but also here and in Section 9.3.4 insofar as they affect rural

economies. Biophysical impacts on non-food cash crops are discussed

below. As with the observed impacts in Section 9.3.2, the future impacts

of climate change described here, and quantified in Section 9.3.4, are

at the latter stages of complex causal chains that flow through changing

patterns of extreme events and/or effects of climate change on biophysical

processes in agriculture and less-managed ecosystems. Lal et al. (2011)

show the regional specificity of projected socioeconomic impacts across

the rural USA, with different regions affected through agriculture, water

stress, and energy costs. Anderson et al. (2010) discuss the complexity

of projected impacts across dryland regions of developing countries.

These considerations increase the uncertainty associated with any

particular impact on the economic base, on land use, or on regional

interconnections.

9.3.3.1. Economic Base and Livelihoods

9.3.3.1.1. General considerations

Climate change will affect rural livelihoods, or “the capabilities, assets

(stores, resources, claims, and access) and activities required for a means

of living” (Chambers and Conway, 1992, p. 6). Many, though by no

means all, rural livelihoods are dependent on natural resources (e.g.,

agriculture, fishing, and forestry), and their availability will vary in a

changing climate. This will have effects on human security and well-

being (Kumssa and Jones, 2010; see also Chapter 12). Climate change

impacts on smallholder and subsistence farmers will be compounded by

environmental and physical processes affecting production at a landscape,

watershed, or community level; and other impacts, including those on

human health and on non-agricultural livelihoods (Morton, 2007) and

also trade and food prices (Anderson et al., 2010). Despite the growing

importance of non-farm livelihoods in rural areas worldwide (Ellis,

2000; Reardon et al., 2007), and households pursuing interdependent

agricultural and non-agricultural livelihoods in peri-urban areas as a

risk management strategy (Lerner and Eakin, 2010; Lerner et al., 2013),

there is a relative scarcity of literature on the interactions of these with

climate variability and climate change.

Climate variability and change interacts with, and sometimes compounds,

existing livelihood pressures in rural areas, such as economic policy,

globalization, environmental degradation, and HIV/AIDS, as has been

shown in Tanzania (Hamisi et al., 2012), Ghana (Westerhoff and Smit,

2009), South Africa (Reid and Vogel, 2006; Ziervogel and Taylor, 2008;

O’Brien et al., 2009), Malawi (Casale et al., 2010), Kenya (Oluoko-

Odingoa, 2011), Senegal (Mbow et al., 2008), and India (O’Brien et al.,

2004). Economic heterogeneity of farm households within communities,

in terms of farm and household size, crop choices, and input use, will be

important in determining impacts (Claessens et al., 2012), as will social

relations within households that affect production (Morton, 2007).

Projected impacts on yields and production of food crops are assessed in

Section 7.4.1 and Figure 7-7. Local warming in excess of 1°C is projected

to have negative impacts in both temperate and tropical regions without

adaptation (though individual locations may benefit). There is medium

confidence in large negative impacts of local increases of 3°C to 4°C,

on productivity, production, and food security, globally and particularly

Table 9-4 | Poverty indicators for rural areas of developing countries. Source: Adapted from IFAD (2010).

Incidence of poverty (%)

Incidence of rural

poverty (%)

Incidence of extreme

poverty (%)

Incidence of extreme

rural poverty (%)

Rural people as % of

those in extreme poverty

1988 2008 1988 2008 1988 2008 1988 2008 1988 2008

eveloping world 69.1 51.2 83.2 60.9 45.1 27.0 54.0 34.2 80.5 71.6

Note: the incidence of extreme poverty and poverty is deﬁ ned as percentage of people living on less than US$1.25 per day and less than US$2 per day, respectively.

624

Chapter 9 Rural Areas

n tropical countries, that go beyond adaptive capacity. The impacts of

climate change on the agricultural sector in Africa, dominated by

smallholder farming and very largely rainfed, are considered to be very

significant to economies and livelihoods (Collier et al., 2008; Hassan,

2010; Kotir, 2011; Müller et al., 2011). These results emerge across a

range of scenarios. Several other studies also map declines in net

revenues from crops and the associated links with food security and

overty (Thurlow and Wobst, 2003; Reid et al., 2008; Molua, 2009;

Thurlow et al., 2009).

Post-harvest aspects of agriculture—storage on-farm and commercially,

handling, and transport—have been relatively neglected in discussions

of climate change, but will be affected by changes in temperature, rainfall,

humidity, and by extreme events. Many adaptation opportunities are

1988

2008

100

Percentage (%)Percentage (%)

RA/R PRPEPERPR/EP

RA/RPRPEPERPR/EP

RA/RP RPEPERPR/EP

RA/RPRPEPERPR/EP

Rural people as percentage of population

Agricultural population as percentage of rural

Incidence of poverty

ERP

R/EPR

A/R

Incidence of poverty in rural areas

Incidence of extreme poverty

Incidence of extreme poverty in rural areas

Rural people as percentage of

those in extreme poverty

(d) Middle East and

North Africa

(b) Latin America and

the Caribbean

(a) Sub-Saharan Africa

Figure 9-2 | Demographic and poverty indicators for rural areas of developing countries, by region (adapted from IFAD, 2010). Shaded countries are those for which data were

available in the original source. Note: Regions used in the source do not correspond with the IPCC regions covered in Chapters 22–30.

625

Rural Areas Chapter 9

lready understood by post-harvest service providers, but getting post-

harvest knowledge into use at scale is a significant challenge (Stathers

et al., 2013; see also Tefera, 2012). Future impacts on production and

storage will affect prices. Food crises in Africa triggered by moderate

declines in agricultural production have been exacerbated by “exchange

entitlement failures”—food price spikes and asset price collapses

(Devereux, 2009). Rising food prices negatively affect many rural people

who are net food buyers (see Table 7-1), and the poorest of the poor in

rural areas—female-headed households (which tend to be poorer than

male-headed households) and those who have limited access to land,

modern agricultural inputs, infrastructure, and education (Ruel et al.,

2010).

The remainder of this section discusses issues around climate impacts

on agricultural livelihoods, other than food crop production: water as

an input to agriculture, non-food crops, livestock, and fisheries.

9.3.3.1.2. Water

Water supply will be impacted through climate change (Chapter 3). In

rural areas groundwater extraction and irrigation water availability is

crucial for agricultural livelihoods but is typically not included in modeled

projections of future crop yields, as discussed by Lobell and Field (2012).

At the same time, non-climate trends including population growth and

lack of adequate regulatory frameworks will greatly affect demand for

water by agriculture and other competing uses, as discussed by

Macdonald (2010) for the southwestern USA, by Juana et al. (2008) for

South Africa, and by multiple authors for the Middle East (Iglesias et al.,

2010; Chenoweth et al., 2011; Sowers et al., 2011; Hanafi et al., 2012;

Rochdane et al., 2012; Verner, 2012).

At the continental level in Africa, analysis of existing rainfall and recharge

studies suggests that climate change will not lead to widespread

catastrophic failure of improved rural groundwater supplies, but it

could affect a population of up to 90 million people, as they live in rural

areas where annual rainfall is between 200 and 500 mm yr

–1

, and where

decreases in annual rainfall, changes in intensity, or seasonal variations

may cause problems for groundwater supply (Macdonald et al., 2009).

At higher resolution groundwater resources are threatened (e.g., in

South Africa; Knüppe, 2011), and multiple water crises are expected to

result from the increasing demand, further affecting people in rural

areas (Nkem et al., 2011). Climate change is expected to impact water

resources in the Asian region in a major way. Immerzeel et al. (2010),

in a study of the Indus, Ganges, Brahmaputra, Yangtze, and Yellow River

basins, conclude that different river basins would experience different

impacts on water availability and food security due to climate change.

They further argue that the Brahmaputra and Indus basins would be

more susceptible to changes in water availability affecting the food

security of 60 million people. In southern Europe, declines in rainfall

and meltwater from glacial ice and snow would increase the costs

of production and living (Falloon and Betts, 2010). Drought could

threaten biodiversity and traditional ecosystems particularly in southern

Europe, with problems exacerbated by declining water quality. Decline

in economic activity may increase rural depopulation and harm the

development of rural communities in southern Europe (Westhoek et al.,

2006).

9.3.3.1.3. Non-food crops and high-value food crops

Non-food crops and high-value food crops, such as cotton, wine grapes,

beverage crops, and other cash crops, which represent an important

source of livelihood in many rural areas, have received less attention

than staple food crops when assessing the impacts of climate change.

Literature on biofuels such as jatropha focuses on the impacts of

biofuels on climate change rather than on the effects of climate on

yields and other relevant variables in these agricultural systems. Where

crops have dual use as food and biofuel (e.g., oilseeds, sugarcane, sugar

beet, maize, and wheat) impacts can be inferred from studies that focus

on their use for food.

The findings of Easterling et al. (2007), that cotton yields would decrease

as changes in temperature and precipitation overcome potential benefits

of increasing carbon dioxide (CO

), have been corroborated in other

findings, such as those of Haim et al. (2008, p. 433) that cotton cultivation

in Israel will decline by 52% and 38% by 2070–2100 under the SRES

A2 and B2 scenarios, and that the net revenue will also decrease by

240% and 173% in both scenarios. Few systematic assessments have

been done on other fiber crops such as jute, kenaf, and flax.

Climate change impacts on wine grapes have been extensively studied

and documented. Climate impacts such as increasing number of hot

days and decreasing frost risk may benefit some varieties. Lobell et al.

(2006) assess the impacts of climate change on yields of six perennial

crops in California by 2099, and report that the production of wine grapes

will experience relatively small changes compared to other commodities

during the concerned period. The uncertainty analysis shows the yield

variations are limited within 10%, although Gatto et al. (2009) argue

that the revenue of the industry in Napa, California, could decline by

2034. Jones et al. (2005) indicate that future climate change will exceed

climatic thresholds affecting ripening for existing varieties grown at the

margins of their climatic limits. Warmer conditions could also lead to

more poleward locations becoming more conducive to grape growing

and wine production.

Lobell and Field (2012) model impacts on 20 perennial crops in California

under the A2 and B1 scenarios; of the four crops with the most reliable

models cherry yields are projected to decline by nearly 20%, strawberries

and table grapes to experience smaller declines, and almonds a slight

positive trend. These projections do not incorporate adaptation options

or possible decline in irrigation water supply, which would limit

production. Yields of several cash crops in the Middle East such as olives,

apples, and pistachios may decline if winter temperatures are too high

(Verner, 2012).

The case of tropical beverage crops, in particular coffee, is discussed in

Box 9-1, and projected changes in area suitable for all three tropical

beverage crops are set out in Table 9-5.

9.3.3.1.4. Livestock

The impacts of climate change on livestock—which form a part of a

variety of farming systems (Devendra et al., 2005)—are seen by

Thornton et al. (2009) as a neglected research area complicated by other

626

Chapter 9 Rural Areas

Box 9-1 | Impacts of Climate Change on Tropical Beverage Crops

The major traded beverage crops coffee, tea, and cocoa support the livelihoods of several million small-scale producers in more than

60 countries of the tropics of Africa, Asia, and Latin America. Coffee production has long been recognized as sensitive to climate

variability, with global production and prices sensitive to occasional frosts in Brazil—the world’s largest producer (Varangis et al., 2003).

Likewise the livelihoods of millions of small producers are dependent both on stability of production and stability in world prices. During

the last crash in coffee prices from 2000–2003 poverty levels in the coffee growing regions of Nicaragua increased, while they fell in the

rest of the country (World Bank, 2003); subsequently during the drought associated with El Niño in 2005 coffee productivity fell to

between a third and half of normal, similarly leading to severely reduced income for small producers (Haggar, 2009).

Gay et al. (2006), analyzing the effects of recent climate change on coffee producing areas in Veracruz, Mexico, have developed

econometric models of the relationship between coffee productivity and ﬂuctuations in temperature and precipitation, which gave an

of 0.69 against historical data. Extrapolating the historical tendencies in temperature and precipitation to 2020 and applying their

econometric model, they predict that coffee production is likely to decline by 34%, and this decline in production takes producers

from making net proﬁts of on average around US$200 per acre to less than US$20 per acre. This has led to a series of studies

projecting the effects of climate change on the distribution of Arabica coffee growing areas of the coming decades summarized

below and in Table 9-5.

For Brazil, Assad et al. (2004) and Pinto et al. (2007) have mapped the changes in area suitable for coffee production in the four main

coffee producing states. A 3°C increase in temperature and 15% increase in rainfall (taken from the general prediction of climate

change for southern Brazil in the IPCC Third Assessment Report of 2001) would lead to major changes in the distribution of coffee

producing zones. In the main coffee producing states of Minas Gerais and São Paulo the potential area for production would decline

from 70 to 75% of the states to 20 to 25%, production in Gioas would be eliminated, but the area would be reduced only by 10% in

Parana. New areas suitable for production in Santa Catarina and Rio Grande do Sul will only partially compensate the loss of area in

other states (Pinto and Assad, 2008). The economic impacts of a rise in temperature of 3°C would cause a 60% decline in coffee

production in the state of São Paulo equal to nearly US$300 million income (Pinto et al., 2007).

Models developed by CIAT predict the distribution of coffee under the A2A climate scenario using a statistical downscaling of the

climate change data from 20 different General Circulation Models (GCMs) used in the IPCC Fourth Assessment. They use WorldClim

data to characterize the current distribution of coffee using 19 climatic variables and then use the climate data downscaled to 1, 5,

and 10 km resolution to map where those conditions may occur in the future (2020 or 2050). This method has been applied to coffee

distribution in Kenya (CIAT, 2010), Central America, and Mexico (Laderach et al., 2010; Glenn et al., 2013); tea production in Kenya

(CIAT, 2011a) and Uganda (CIAT, 2011b); and cocoa production in Ghana and Côte d’Ivoire (CIAT, 2011c; Laderach et al., 2013)

(Table 9-5). The suitability for coffee crops in Costa Rica, Nicaragua, and El Salvador will be reduced by 40% (Glenn et al., 2013)

while the loss of climatic niches in Colombia will force the migration of coffee crops toward higher altitudes by mid-21st century

(Ramirez-Villegas et al., 2012). In the same way, increases in temperature will affect tea production, in particular at low altitudes

(Wijeratne, et al., 2007). Only one similar study has been done for Robusta coffee (Simonett, 2006), in Uganda, which shows similarly

drastic changes in both distribution and total area suitable for coffee production.

Effects are also expected on the incidence of pests and diseases in these crops. Increased generations under climate change for the

coffee nematode have been predicted for Brazil (Ghini et al., 2008). Jaramillo et al. (2011) conclude that Coffee Berry Borer

(Hypothenemus hampei) distribution in East Africa has expanded as a result of rising temperatures, and predicts, based on A2A and B2B

scenarios of Met Ofﬁce Hadley Centre climate prediction model 3 (HadCM3), that it will spread to affect the main coffee producing

areas of Ethiopia, Kenya, Uganda, Rwanda, and Burundi by 2050.

Continued next page

627

Rural Areas Chapter 9

drivers of change, rapid change in livestock systems, spatial heterogeneity,

and social inequality between livestock keepers. They review various

pathways of impact on livestock. Impacts through drought will be

significant, as will heat stress, particularly of Bos taurus cattle. Impacts

through animal health and disease will be even harder to predict than

other categories of impact (Thornton et al., 2009). Franco et al. (2011)

reveal significant declines in forage for ranching in California under

SRES scenarios B1 and A2.

Pastoralists, who are dependent on livestock grazed in arid, semiarid,

or mountainous areas, display very specific combinations of adaptive

capacity, especially through mobility and vulnerability, as discussed in

Section 9.3.5. Ericksen et al. (2012), with particular reference to East

Africa, discuss possibilities of loss of rangeland productivity, changes

in rangeland composition toward browse species, and changes in herd

dynamics through more frequent droughts as possible impacts. In the

Middle East, rangelands will be under substantial climate stress, which

may reduce their carrying capacity, in light of the growing demand for

meat products and the region’s growing livestock population (Verner,

2012, p. 166). Little et al. (2001) discuss impacts of floods, directly and

through disease, on pastoral herds. Similarly in the Ferlo Region in

northern Senegal, modest reduction in rainfall of 15% in combination

with a 20% increase in rainfall variability could have considerable

effects on livestock stocking density and profits, reducing the optimal

stocking density by 30%, based on six GCMs (Hein et al., 2009).

As extensive livestock production is associated with semiarid areas

marginal for cropping, some authors project shifts toward livestock

production under climate change. Modeled data from across Africa on

the net income per unit of land from crops and different livestock

species show that farmers are more likely to keep livestock, compared

to crop cultivation, as temperatures increase and as precipitation

decreases. Within livestock production, beef production will decline and

sheep and goat production increase (Seo and Mendelsohn, 2007a). Large-

scale commercial beef cattle farmers are most vulnerable to climate

change, particularly because they are less likely to have diversified

(Seo and Mendelsohn, 2007b). Kabubo-Mariara (2009) shows for non-

pastoral areas of Kenya the nonlinear relationship of livestock production

to climate change, whereby increased mean precipitation of 1% could

reduce revenues from livestock by 6%. Jones and Thornton (2009)

identify major transition zones across Africa where increased probability

of drought up to 2050 will create conditions for shifts from cropping to

livestock.

9.3.3.1.5. Fisheries

Impacts of climate change on aquatic ecosystems will have adverse

consequences for the world’s 36 million fisherfolk, through multiple

pathways including changes in fish stock distribution and abundance,

and destruction of fishing gear and infrastructure in storms and severe

Box 9-1 (continued)

At a minimum climate change will cause considerable changes in the distribution of these crops, disrupting the livelihoods of millions

of small-holder producers. In many cases the area suitable for production would decrease considerably with increases of temperature

of only 2°C to 2.5°C. Although some local areas may experience improved conditions for coffee production, for example, high-altitude

areas of Guatemala, the overall predictions are for a reduction in area suitable for coffee production by 2050 in all countries studied

(Laderach et al., 2010).

Crop Countries Change in climate by 2050 Change in total area by 2050

Change in distribution by 2050

(in meters above sea level)

Coffee

Guatemala, Costa Rica,

Nicaragua, El Salvador,

Honduras, Mexico

2.0–2.5°C increase in temperature

5–10% decline in total rainfall

Between 38% and 89% decline in area suitable

for production

Minimum altitude suitable for production rise

from 600 to 1000

Kenya

2.3°C increase in temperature

Rainfall increase from 1405 mm to 1575 mm

Substantial decline in suitability of western

highlands, some decline in area optimal for

production in eastern highlands

Minimum altitude for production rise from

1000 to 1400

Tea

Kenya

2.3°C increase in temperature

Rainfall increase from 1655 mm to 1732 mm

Majority of western highlands lose suitability,

while losses are compensated by gains at

higher altitude in eastern highlands

Optimum altitude for production change from

1500–2100 to 2000–2300

Uganda

2.3°C increase in temperature

Rainfall increase from 1334 mm to 1394 mm

Considerable reduction in suitability for

production across all areas

Optimal altitude change from 1450–1650 to

1550–1650

Cocoa

Ghana, Côte d’Ivoire

4,5

2.1°C increase in temperature

No change in total rainfall

Considerable reduction in area suitable for

production; almost total elimination in Ivory

Coast without adaptation measures

Optimal altitude change from 100–250 to

450–500

Table 9-5 | Projected changes in areas suitable for production of tropical beverage crops by 2050.

Sources:

CIAT (2010);

CIAT (2011a);

CIAT (2011b);

CIAT (2011c);

Laderach et al. (2013);

Glenn et al. (2013). Projections use the SRES A2 scenario; the projection

methodology is described in Box 9-1.

628

Chapter 9 Rural Areas

eather events (Badjeck et al., 2010; see also Sections 5.4.3.3, 6.4.1.1,

7.4.2, 30.6.2.1). An indicator approach (assessing climate change

impacts together with the high share of fisheries as a source of income)

showed that economies with the highest vulnerability of capture

fisheries to climate change were in central and western Africa

(e.g., Malawi, Guinea, Senegal, and Uganda), Peru and Colombia in

northwestern South America, and four tropical Asian countries

(Bangladesh, Cambodia, Pakistan, and Yemen) (Allison et al., 2009). In

China, Japan, and South Korea, changes in climate and social systems

could have a negative impact on fisheries, adversely affecting livelihoods

and food security of the region (Kim, 2010).

9.3.3.2. Infrastructure

Assessments of the impacts of climate change on infrastructure take a

general or urban perspective and do not focus on rural areas, though

rural impacts can be inferred. River flooding and sea level rise will produce

temporary loss of land and land activities, and damage to transportation

infrastructure particularly on coastal areas (Kirshen et al., 2008), with

specific evidence from North America (Hess et al., 2008). Flooding events

may cause sediment transport and damage roads and bridges (Nearing

et al., 2004) as well as affecting reservoir storing capacity. Importantly,

in rural areas usually there are few alternatives once a road is blocked

and that may increase vulnerability of rural areas when facing extreme

hydroclimatological events that impact transportation infrastructure

(NRC, 2008). Climate change will affect the operation of existing water

infrastructures (Kundzewicz et al., 2008). Some documented impacts on

dams, reservoirs, and irrigation infrastructure include reduction of sediment

load due to reductions in flows (associated with lower precipitation),

positively affecting infrastructure operation (Wang et al., 2007); impacts

of climate variability and change on storage capacity that creates further

vulnerability (Lane et al., 1999); and failures in the reliability of water

allocation systems (based on water use rights) due to reductions of

streamflows under future climate scenarios (Meza et al., 2012).

In Arctic Canada and Alaska, infrastructure built for very cold weather

will deteriorate as the air and ground warm. Larsen et al. (2008)

estimate, using the Atmosphere-Ocean General Circulation Model

(AOGCM) intercomparison project and an A1B scenario, increases in

public infrastructure costs of 10 to 20% through 2030 and 10% through

2080 for Alaska, amounting to several billion dollars, much of it to be

spent outside of urban centers. Lemmen et al. (2008) reports that

foundation fixes alone in the largely rural Northwest Territories could

cost up to CAN$420 million, and that nearly all of northern Canada’s

extensive winter road network, which supplies rural communities and

supports extractive industries which bring billions of dollars to the

Canadian economy annually, is at risk (Furgal and Prowse, 2008) from

a 2°C to 4°C change in ground surface temperatures, which would imply

a cost of replacement with all-weather roadways of CAN$85,000 per

kilometer, over several decades.

9.3.3.3. Spatial and Regional Interconnections

In both developing and developed countries, rural areas have been

increasingly integrated with the rest of world. The main channels

hrough which this rapid integration process takes place are migration

(permanent and cyclical), commuting, transfer of public and private

remittances, regional and international trade, inflow of investment, and

diffusion of knowledge through new information and communication

technologies (IFAD, 2010), as well as the spatial intermingling of rural

and urban economic activities (see Box CC-UR).

9.3.3.3.1. Migration

It is difficult to establish a causal relationship between environmental

degradation and migration (see Section 12.4.1). Many authors argue

that migration will increase during times of environmental stress (e.g.,

Brown and Crawford, 2008; Afifi, 2011; Kniveton et al., 2011; Gray and

Mueller, 2012), and will lead to an increase in abandonment of settlements

(McLeman, 2011). Climate variability has been associated with rural-

urban migration (Mertz et al., 2011; Parnell and Walawege, 2011).

Another body of literature argues that migration rates are no higher

under conditions of environmental or climate stress (Cohen, 2004;

Brown, 2008; van der Geest and de Jeu, 2008; Tacoli, 2009; McLeman

and Hunter, 2010; Black et al., 2011a,b; Foresight, 2011; Gemenne,

2011; van der Geest, 2011). For Tacoli (2009) the current alarmist

predictions of massive flows of so-called “environmental refugees” or

“environmental migrants” are not supported by past experiences of

responses to droughts and extreme weather events, and predictions

for future migration flows are tentative at best. Analogies with past

migration experiences are used frequently in such studies (McLeman

and Hunter, 2010). For example, in Ghana the causality of migration

was established to be relatively clear in the case of sudden-onset

environmental perturbations such as floods, whereas in case of slow-

onset environmental deterioration, there was usually a set of overlapping

causes—political and socioeconomic factors—that come into play (van

der Geest, 2011). Similarly, a recent survey by Mertz et al. (2010) has

argued that climate factors played a limited role in past adaptation

options of Sahelian farmers. Given the multiple drivers of migration

(Black et al., 2011a,b) and the complex interactions that mediate

migratory decision making by individual or households (McLeman and

Smit, 2006; Raleigh, 2008; Black et al., 2011a,b; Kniveton et al., 2011),

the projection of the effects of climate change on intra-rural and rural-

to-urban migration remains a major challenge.

9.3.3.3.2. Trade

Agricultural exports accounted for around one-sixth of world agricultural

production in 2012, while this proportion was higher for some

commodities such as oilseeds, sugar, and fish (OECD and FAO, 2013).

Global agricultural exports grew at an average annual rate of 9% in

2000–2005 and 11% in 2005–2011 (WTO, 2013, pp. 63–72). Apart from

a major price hike and high price volatility since 2007–2008, several

structural and cyclical factors—such as droughts in major producers,

expansion of area under biofuel crop production, financial speculation,

export restrictions—have led to volatility and unpredictability in the

trading environment (Chapter 7; see also Abbott, 2008; FAO, 2008;

Cooke and Robles, 2009; Karapinar and Haberli, 2010; Schmidhuber and

Matuschke, 2010; Timmer, 2010; Headey, 2011;Wright, B.D., 2011;

Anderson and Nelgen, 2012; Nazlioglu, 2013). In the absence of

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xtensive literature and reliable data on within-country trade, this

section focuses on international trade in the specific context of climate

change.

There is limited evidence and medium agreement that climate change

will affect trade patterns and it will increase international trade volumes

in both physical and value terms by altering the comparative advantage

of countries and regions, and given its potential impacts on agricultural

prices (Nelson et al., 2009b, 2010, 2013; Tamiotti et al., 2009). For

example, simulation based results from variants of the National Center

for Atmospheric Research (NCAR) and Commonwealth Scientific and

Industrial Research Organisation (CSIRO) climate models (A2 scenario)

suggest that climate change might lead to increases in export volumes

(of rice, wheat, maize, millet, sorghum, and other grains) from developed

to developing countries by 0.9 million Mtonnes to 39.9 million Mtonnes

by 2050. Higher export volumes are expected if future scenarios

consider CO

fertilization effects, as they produce lower world prices

than scenarios without CO

effects. Many regions including South Asia,

East Asia and Pacific, Middle East, North Africa, and sub-Saharan Africa

are projected to increase their imports substantially over this period

(Nelson et al., 2009b, 2010).

The recent literature highlights the potential role of trade in adaptation

to climate impacts on global crop yields, while cautioning policy makers

about the possible negative consequences of increased trade (Verburg

et al., 2009; Lotze-Campen et al., 2010; Huang et al., 2011; Schmitz et

al., 2012). Importing food might help countries adjust to climate change-

induced domestic productivity shocks and mitigate related welfare

losses (Reimer and Li, 2009; Tamiotti et al., 2009). Countries might also

capitalize on new export opportunities arising from higher achievable

yields, for example in Argentina (Asseng et al., 2013), or increasing

heterogeneity of climate impacts on yields in neighboring countries, for

example in Tanzania (Ahmed et al., 2012). Increased trade would lower

the cost of food and thus help alleviate food insecurity; however, if it is

driven by an expansion of agricultural areas (especially to marginal land

and to forests), it would also lead to negative environmental consequences

in the form of loss of biodiversity, deforestation, and additional carbon

emissions (Verburg et al., 2009; Lotze-Campen et al., 2010; Schmitz et

al., 2012).

If climate change affects crop yields negatively, and results in increased

frequency of extreme events (IPCC, 2012; see also Chapter 3), especially

in low-income developing countries, the consequent short-term food

deficits might need to be supplied, fully or partly, through food aid

(Alderman, 2010). Hence food aid agencies, such as the United Nations

World Food Programme, might face additional operational challenges

(Barrett and Maxwell, 2006; Harvey et al., 2010). Local or regional

procurement of food aid, targeted distribution of food, and safety net

programs through direct income transfers could be part of an overall

strategy to address climate-induced shocks to food security (see also

Chapter 7) (Alderman, 2010; Harvey et al., 2010).

The potential impacts of climate change on agricultural trade and the role

that trade could play in adaptation will inevitably depend on countries’

trade policies. There is medium evidence and medium agreement that

deepening agricultural markets through trade reform, improved market

access, avoiding export controls, and developing institutional mechanisms

o improve the predictability and the reliability of the world trading

system as well as investing in additional supply capacity of small-scale

farms in developing countries could help reduce market volatility and

offset supply shortages that might be caused by climate change (Reimer

and Li, 2009; Tamiotti et al., 2009; UNEP, 2009; Karapinar, 2011, 2012;

Tanaka and Hosoe, 2011; Ahmed et al., 2012).

9.3.3.3.3. Investment

Climate change may also affect investment patterns in rural areas. On

the one hand, countries, regions, and sectors that are expected to be

affected adversely by climate change may have difficulty attracting

investment. On the other hand, ecological zones that will become

favorable as a result of climate change are expected to see increasing

inflow of investment. The recent price hikes in agricultural commodities

have led to new initiatives of foreign direct investment (FDI) in large-

scale crop production (World Bank, 2010b; Anseeuw et al., 2012), with

capital-endowed countries with high food imports investing in large

production projects in low-income countries endowed with low-cost

labor forces and land and water resources. Climate change will lead to

similar investment patterns. However, there is a risk that these new

investments might not be integrated into local structures and that local

populations will become increasingly vulnerable as they lose access to

vital assets such as land and water (Anseeuw et al., 2012).

9.3.3.3.4. Knowledge

Rural areas are increasingly exposed to diffusion of knowledge through

migration, trade and investment flows, technology transfers, and

improved communication and transport facilities (IFAD, 2010), although

differentials on knowledge access and diffusion (e.g., access to high-

speed Internet) between rural and urban areas remain, even in high-

income countries. Future impacts of climate change on these channels

of integration will affect the pace and intensity of knowledge transfers.

If trade, migration, and investment flows will be intensified as a result

of climate change, this will have a positive impact on knowledge

transfer both from and to rural areas.

Traditional knowledge (TK) developed to adapt to past climate variability

and change can both be affected by climate change and used and

transformed in adaptation (Nyong et al., 2007). Ettenger (2012)

discusses how seasonal hunting camps among the Cree of Northern

Quebec that were the occasion for intergenerational knowledge transfer

have been disrupted by changing bird migrations, while new technologies

such as the Internet, GPS, and satellite phones have been integrated into

livelihood strategies. Climate change-induced migration can threaten TK

transfer (Valdivia et al., 2010; Gilles et al., 2013). Disaster management by

central government may undermine decentralization efforts, disfavoring

TK transfer (Dekens, 2008).

9.3.3.4. Second-Order Impacts of Climate Policy

Policy responses for mitigation and adaptation affect rural people and

their livelihoods and environments. Working toward increasing energy

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supply from renewable resources may result in landscape changes

(Dockerty et al., 2006; Prados 2010); increasing employment opportunities

(del Río and Burguillo, 2008); or increasing conflicts for scarce resources,

such as water (Gold and Bass, 2010; Blair et al., 2011; McIntyre and

Duane, 2011; Phadke, 2011). Planning applications for wind energy

schemes in the UK have been subject to local opposition when they are

perceived as having negative impacts on rural landscape qualities (van

der Horst, 2007; Wolsink, 2007; Jones and Eiser, 2010). Governance of

energy distribution is thus an important issue (Vermeylen, 2010; Devine-

Wright, 2011). Steps toward energy self-sufficiency can reinforce rural

autonomy in isolated rural communities, including indigenous groups

(Love and Garwood, 2011).

Social responses to such changes are expected (Molnar, 2010). The

promotion of biofuel crops has been an extremely controversial issue

during 2000–2010, as they have potential socioeconomic impacts related

to their asserted ability to act as stimulus for rural economies, promote

changes in land ownership, and affect food security (German et al.,

2011). Delucchi (2010) concludes that biofuels produced from intensive

agriculture will aggravate stresses on water supplies, water quality, and

land use, and impact rural areas (through land use change) and agriculture

(see also Box CC-WE). Concerns about the impact of biofuel production

on food security relates to increases in food prices, land concentration

(and landgrabs), and competition for water (Eide, 2008; Müller et al.,

2008; German et al., 2011). Gurgel et al. (2007), who modeled potential

production and implications of a global biofuels industry by the end of

the century under a reference scenario and a high-mitigation scenario,

recognized the need for a high land conversion rate to achieve moderate

objectives. Delucchi (2010) suggests developing biofuels programs with

low inputs of fossil fuels and chemicals, that do not require irrigation,

and on land with little or no economic or ecological opportunity cost

(Plevin et al., 2010). This implies analyzing each case in its context,

including production for both local and global markets, and factoring

in concerns for social, cultural, and economic costs of biofuel production

(i.e., impact of biofuel production on indigenous livelihoods and culture).

International mechanisms for emission reduction through forest and

land management have been developed under the global initiative

Reducing Emissions from Deforestation and Forest Degradation (REDD),

now REDD+. These mechanisms are designed to use market tools (e.g.,

payment for ecosystem services) to reduce emissions, while providing

social co-benefits following the principles of effectiveness, efficiency, and

equity (Brown, D. et al., 2008; Hall, 2012; Hoang et al., 2013). However,

there have been many criticisms that the rural poor are excluded from

participation (Campbell, 2009; Sikor et al., 2010; van Noordwijk et al.,

2010; Hall, 2012); and that lack of community participation can undermine

a general decentralization of forest management (Phelps et al., 2010).

9.3.4. Valuation of Climate Impacts

This section assesses studies that have adopted various economic

methods for valuation of impacts of climate change on rural areas. This

is a difficult task and should reflect the significance of the ecological

service categories for different stakeholders, including women (Kennet,

2009) and minority groups, and ideally the valuations of unit changes

Frequently Asked Questions

FAQ 9.2 | What will be the major climate change impacts in rural areas across the world?

The impacts of climate change on patterns of settlement, livelihoods, and incomes in rural areas will be complex

nd will depend on many intervening factors, so they are hard to project. These chains of impact may originate

with extreme events such as ﬂoods and storms, some categories of which, in some areas, are projected with high

conﬁdence to increase under climate change. Such extreme events will directly affect rural infrastructure and may

ause loss of life. Other chains of impact will run through agriculture and the other ecosystems (rangelands, ﬁsheries,

wildlife areas) on which rural people depend. Impacts on agriculture and ecosystems may themselves stem from

extreme events like heat waves or droughts, from other forms of climate variability, or from changes in mean climate

onditions such as generally higher temperatures. All climate-related impacts will be mediated by the vulnerability

of rural people living in poverty, isolation, or with lower literacy, and so forth, but also by factors that give rural

communities resilience to climate change, such as indigenous knowledge, and networks of mutual support.

Given the strong dependence in rural areas on natural resources, the impacts of climate change on agriculture,

forestry, and ﬁshing, and thus on rural livelihoods and incomes, are likely to be especially serious. Secondary

(manufacturing) industries in these areas, and the livelihoods and incomes that are based on them, will in turn be

substantially affected. Infrastructure (e.g., roads, buildings, dams, and irrigation systems) will be affected by extreme

events associated with climate change. These climate impacts may contribute to migration away from rural areas,

though rural migration already exists in many different forms for many non-climate-related reasons. Some rural

areas will also experience secondary impacts of climate policies—the ways in which governments and others try to

reduce net greenhouse gas emissions such as encouraging the cultivation of biofuels or discouraging deforestation.

These secondary impacts may be either positive (increasing employment opportunities) or negative (landscape

changes, increasing conﬂicts for scarce resources).

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Rural Areas Chapter 9

n the levels of those services across management options. Valuations

can be made at individual or communal levels (Farber et al., 2006) and

often involve complexities with regard to the use of social discount rates

for comparing intergenerational effects over varying time horizons

(Dasgupta, 2011). Different understandings of value, and different

philosophical approaches to address it, may exist (Weisbach and Sunstein,

2008; Kosoy and Corbera, 2010; Spangenberg and Settele, 2010), which

makes it more difficult to agree on valuation methodologies. The

impacts of climate change are expected to be unequally distributed

across the globe, with developing countries at a disadvantage, given

their geographical position, low adaptive capacities (Stern, 2007; World

Bank, 2010a) and the significance of agriculture and natural resources

to the economies and people (Collier et al., 2008; World Bank, 2010a).

Both direct and indirect impacts have been projected, such as lower

agricultural productivity, increase in prices for major crops, and rise in

poverty (Hertel et al., 2010), which have implications for rural areas and

rural communities. This section discusses the valuation of impacts with

reference to agriculture, fisheries and livestock, water resources, mining,

extreme weather events and sea level rise, recreation, tourism, and

forestry. There are various channels through which changes in economic

values may occur in rural areas, such as through changes in profitability,

crop and land values, and loss of livelihoods of specific communities

through changes in fisheries and tourism values. Losses and gains in

health status and nutrition, and wider economy-wide impacts such as

changes in job availability and urbanization, also impact economic values

that accrue to rural communities, the opportunities and the constraints

that rural communities experience, and changes that rural landscapes

undergo. Because rural areas are included, but not exclusively dealt with

in calculations of economy-wide gross domestic product (GDP) losses

due to climate change impacts, these are not dealt with separately in

this chapter. Studies on the health impacts of climate change for the

most part do not distinguish between rural and urban areas, although

there are specific vulnerabilities that communities in rural areas face

arising from a variety of factors such as remoteness, lack of access to

services, and dependence on certain occupations such as farming which

are dealt with in Section 11.3. The impact on availability of freshwater

resources is another major area of concern for the developing regions

in particular. Climate change can adversely impact poverty through

multiple channels (Sections 10.9, 13.2).

Viewing impacts regionally, despite the ongoing debates around the

uncertainty and limitations of valuation studies, scholars generally agree

that some African countries could experience relatively high losses

compared to countries in other regions (Collier et al., 2008; Watkiss et

al., 2010; World Bank, 2010a). These conclusions emerge across a range

of climate scenarios and models used by researchers. For instance,

Watkiss et al. (2010) use the FUND model for a business-as-usual

scenario and a scenario of mitigation to 450 ppm and 2°C global mean

temperature increase as generated by the PAGE2002 model, while the

World Bank uses a range of country specific models for calculating costs.

Global costs including adaptation costs are calculated for an approximately

2°C warmer world by 2050 for Mozambique, Ethiopia, Ghana, Bolivia,

Vietnam, Samoa, and Bangladesh. Overall negative consequences are

seen for Africa and Asia, due to changes in rainfall patterns and increases

in temperature (Müller et al., 2011). Though climate change and climate

variability would impact a range of sectors, water and agriculture are

expected to be the two most sensitive to climatic changes in Asia (Cruz

t al., 2007; see also Chapter 3) and for droughts in particular for

Australia (Meinke and Stone, 2005; Nelson et al., 2007). In Latin American

and Caribbean countries, higher temperatures and changes in precipitation

patterns associated with climate change affect the process of land

degradation, compromising extensive agricultural areas. Research on

climate change impacts in rural North America has largely focused on

the effects on agricultural production and on indigenous populations,

many of whom rely directly on natural resources. Developed countries

in Europe will be less affected than the developing world (Tol et al.,

2004), with most of the climate sensitive sectors located in rural areas.

Valuation and costing of climate impacts draw upon both monetary and

non-monetary metrics. Most studies use models that estimate aggregated

costs or benefits from impacts to entire economies, or to a few sectors,

expressed in relation to a country’s GDP (Stage, 2010; Watkiss, 2011).

Values that are aggregated across sectors generalize across multiple

contexts and could mask particular circumstances that could be

significant to specific locations, while expressing outcomes in aggregated

GDP terms. This is a matter of concern for economies in Africa and Asia,

where subsistence production continues to play a key role in rural

livelihoods. Valuation of non-marketed ecosystem services poses further

methodological and empirical concerns (Dasgupta, 2008, 2009; Stage,

2010; Watkiss, 2011). Würtenberger et al. (2006) developed a methodology

to estimate environmental and socioeconomic impacts of agricultural

trade regarding virtual land use, and Adger et al. (2011) use qualitative

methodologies to consider non-market metrics of risk, focusing on place-

and identity-based principles of justice, which recognize individual and

community identity in decision making.

Integrated assessment models and cost-benefit tools have been criticized:

for being inadequate to assess intergenerational events, or processes

with high levels of uncertainty and irreversibility; for not considering

equity concerns and power structures; for assigning monetary values on

the basis of incomplete information or assuming speculative judgments

regarding the monetary value of, for example, natural resources

(Kuik et al., 2008; Ackerman et al., 2009); and for not recognizing

incommensurability (Aldred, 2012). In recent years, various perspectives

for valuing the economic impacts of climate change have come into

focus including the feminist (Nelson, 2008; Power, 2009), deliberative

(Zografos and Howarth, 2010), or behavioral economics-based (Brekke

and Johansson-Stenman, 2008; Gowdy, 2008), and the integration of

economics with moral and political philosophy (Dietz et al., 2008).

Some common characteristics of these new approaches include

interdisciplinarity, acknowledging the diversity of views, and maintaining

complexity in models. Research in this area, although relatively recent,

shows promise. Illustrative regional and sub-regional estimates for the

value of agricultural and non-agricultural impacts of climate change, as

available in the literature, are presented here.

9.3.4.1. Agriculture

Changes in agricultural production will have corresponding impacts on

incomes and well-being of rural peoples. The largest known economic

impact of climate change is on agriculture because of the size and

sensitivity of the sector, particularly in the developing world and to a

lesser extent in parts of the developed world. A large number of studies

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Chapter 9 Rural Areas

o evaluate the impacts on the agricultural sector and its ramifications

for communities have been conducted at various scales, ranging from

micro-level farm models to large-scale regional and country level climate

cum socioeconomic scenario modeling exercises. Some of these also

report values for associated economic losses.

Since models are simplifications of complex real-world phenomena,

different models tend to highlight different aspects of impacts and their

consequent economic values. For instance, in estimating economic losses

the Ricardian method has been used widely to study climate change

impacts (with adaptation inbuilt) in agriculture. However, often such

analysis does not incorporate features like technological progress, relative

price changes, agricultural policy, and other dynamic characteristics.

Similarly on the biophysical impacts side, changes in the El Niño-Southern

Oscillation (ENSO) statistics may also have serious economic implications

for the agricultural sector in certain countries such as in Latin America

and Australia (Kokic et al., 2007). However, ENSO responses differ strongly

across climate models, and at the current stage of understanding do

not allow conclusions to be drawn on how global warming will affect

the Tropical Pacific climate system (Latif and Keenlyside, 2009). A sample

of the available studies is provided in Table 9-6.

9.3.4.2. Other Rural Sectors: Water, Fisheries, Livestock, Mining

The changes in valuation of water resources due to climate change arise

from expected impacts on populations dependent on these water

resources and these will be felt in several parts of the world (Sections

3.4.9, 3.5, 3.8). Monetary estimates of losses due to impacts on water

resources are not generalizable. Among alternative approaches to value

water resources, use of the water footprint tool (Hoekstra and Mekonnen,

2012), which measures human utilization of water by a nation, and the

concept of virtual water have been suggested for informing policy

makers in water-scarce countries, such as Egypt.

Analysis of intergenerational valuation has provided some interesting

results in valuation of marine fisheries (Ainsworth and Sumaila, 2005).

For fisheries in rural coastal areas, some of the challenges faced include

the valuation of environmental externalities such as breeding habitats,

or mangroves, that might be lost due to climate change or other forces

(Hall, 2011). It has also been argued that the true worth of livelihoods

dependent on fisheries in developing countries, where these constitute

part of a diversified livelihood or subsistence strategy, requires a different

set of metrics from those used in the developed world (Mills et al.,

Findings and estimates Country/region and model /scenario Study

Annual economic loss in rice production: $54.17 million Malaysia (2°C rise in temperature) Vagheﬁ et al. (2011)

GDP reduction from loss of agricultural productivity by 2080: 1.4%;

welfare loss: 1.7%

Southeast Asian countries: Thailand, Vietnam, Philippines, Singapore,

Malaysia, Indonesia (dynamic CGE)

Zhai and Zhuang (2009)

Decline in food grain production between 2030 and 2050 by up to 18% India (SRES A1B scenario) Dasgupta et al. (2013)

Annual spending for coping with adverse agricultural impacts between

2010 and 2050: US$4.2–5 billion

Asia (various scenario based estimates) ADB and IFPRI (2009)

Decline in farmland values for each degree Celsius of warming: 4–6000

pesos

Mexico (Ricardian analysis) Mendelsohn et al. (2010)

Fall in crop land values for rural communities: 13% USA (10% average increase in temperature) Mendelsohn et al. (2007)

Mixed effects with some improved proﬁ ts Canada (increasing precipitation) Mendelsohn and Reinsborough (2007)

Adverse impacts on farming USA (increasing temperature) Mendelsohn and Reinsborough (2007)

Crop losses under drought: CAN$7–171 per hectare Canada (Canadian Global Model 2) Wittrock et al. (2011)

Annual agricultural losses up to $3 billion

Flooding increases losses

California (SRES B1 (low emissions) and SRES A2 (medium emissions)

scenarios)

Franco et al. (2011)

Damages to agriculture, hydropower, and infrastructure (including coastal

areas) by 2050: US$7.6 billion

Mozambique (dynamic CGE model) World Bank (2010a)

Decline in gross domestic product (GDP) from agriculture and linked

sectors: 10% from benchmark levels

Ethiopia (Cline, CGCM2, and PCM) Mideksa (2010)

By 2100: total losses of US$48.2 billion to gains of US$90 billion

In 2020 for 1.6% warmer and 3.7% drier climate: net farm revenues

decline by up to 25%

11 African countries (Ricardian analysis; various climate scenarios) Dinar et al. (2008)

Decline in daily per capita calorie availability by up to 10% in 2050 Developing countries (SRES A2 scenario; CSIRO and NCAR models) Nelson et al. (2009)

Losses in gross value of production up to 25% (Guatemala, followed by

other countries)

Guatemala, Belize, Costa Rica, Honduras (SRES A2 and B2; Regional

climate models)

UN ECLAC (2010a,b)

Loss in incomes of farmers by 2020: 14%; by 2060: 20% South America (SRES A1; Canadian Climate Centre) Seo and Mendelsohn (2008)

Annual damages between 1% and 39% in farm property values Brazil (climate predictions from 14 GCMs) Sanghi and Mendelsohn (2008)

Varying impacts across regions; declining agricultural crop productivity

in some

Southern Europe (IPCC AR4 climate projections; qualitative assessment ) Falloon and Betts (2010)

Large variation in impacts on crops in Europe by 2050, mostly negative Most affected: Hungary, Serbia, Bulgaria, Romania (expert evaluation;

climate predictions from RCMs)

Olesen et al. (2011)

Table 9-6 | Illustrative sample of studies on economic value and changes in value from climate change impacts in the agriculture sector.

Notes: CGCM2 = Coupled General Circulation Model 2; CGE = Computable General Equilibrium; CSIRO = Commonwealth Scientiﬁ c and Industrial Research Organisation; GCM

= General Circulation Model; NCAR = National Center for Atmospheric Research; RCM = Regional Climate Model; SRES = Special Report on Emission Scenarios.

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Rural Areas Chapter 9

011). Climate change can also have significant impacts on livestock

production (Section 9.3.3.1).

A relatively less researched area which may impact the livelihoods of

rural communities is mining (Section 26.11.1.2). Economic viability of

mining enterprises as well as communities dependent on them is

vulnerable to climate change. Pearce et al. (2011) highlight concerns

for Canada, where mining is a rural activity with few other available

economic activities while Damigos (2012) finds economic losses for

mining in the Mediterranean region and Greece in particular. Current

and past infrastructure for mines was built under a no-climate change

presumption and economic and ecological vulnerabilities as a result are

substantial, and industry actors are unprepared to deal with this. There

is little research on impacts in mining sectors in the USA and Mexico.

Changes in the energy and water sector present a complex mix of risks

and opportunities for primary extraction and processing industries. Site

management, transport of supplies and resources to and from mines,

exploration activities, and their associated costs would determine the

extent of loss, along with the importance of the sector in the local

economy (Backus et al., 2012).

9.3.4.3. Extreme Weather Events, Sea Level Rise

The climate change-related extreme events that may cause changes in

economic values in rural areas include heat waves and droughts, storms,

inundation, and flooding (Stern, 2007; Handmer et al., 2012; see also

Section 3.4.9). A detailed discussion on the costs of climate extremes

and disasters is set out by Handmer et al. (2012). Costs can be of two

kinds: losses or damage costs and costs of adaptation. While some of

the costs lend themselves to monetary valuation (such as infrastructure

costs), others cannot be easily estimated such as the value of lives

lost and the value of ecosystem services lost (for discussion on the

methodologies for valuing costs refer to Handmer et al., 2012; see also

Section 4.5.3).

Damage costs of floods and droughts (Section 10.3.1) and from sea

level rise in Europe (Swiss Re, 2009) demonstrate the cost implications

for rural communities in the developed regions of the world. Studies

mapping the adverse impacts in UK and elsewhere in Europe show a

range of sectors that are impacted in rural areas particularly due to

drought in Europe and flooding in UK, with the worst effect being on

summer crops in Mediterranean regions (Giannakopoulos et al., 2009).

Longer term adaptation could reduce the severity of losses but could

include displacement of agricultural and forestry production from

southern Europe to the North. The UK Government’s Foresight

Programme (Foresight, 2004) estimates that global warming of 3°C to

4°C could increase flood damage costs from 0.1% up to 0.4% of GDP.

Much of the investment in flood defenses and coastal protection would

be in rural coastal areas.

Several studies from the developing countries provide evidence on the

substantial costs rural communities in particular face in these countries.

Salinity and salt water intrusion have implications for rural livelihoods

as they impact both fisheries and agriculture (Section 5.5.3). Sea level

rise also leads to wetland loss and coastal erosion. A few illustrations

of the range of impacts of relevance for the rural economy are provided

ere. Loss of agricultural land and changes in the saline-freshwater

interface is estimated to impact the economies of Africa adversely

(Dasgupta, S. et al., 2009; SEI, 2009). Ahmed et al. (2009) suggest that

climate volatility from increase in extreme events increases poverty in

developing countries, particularly Bangladesh, Mexico, Indonesia, and

countries in Africa. They also find that on simulating the effect of climate

extremes on poverty in Mexico using the A2 scenario as generated by

a Coupled Model Intercomparison Project Phase 3 (CMIP3) multi-model

data set, rural poverty increases by 43 to 52% following a single climate

shock due to climate extremes. Studying extreme events, Boyd and

Ibarrarán (2009) use a CGE model to simulate the effects of persistent

droughts on the Mexican economy and find declines in production of

10 to 20% across a variety of agricultural sectors between 2005 and

2026. Scenario-based stakeholder engagement has been tested for

coastal management planning under climate change threats (Tompkins

et al., 2008) and to determine impacts and responses of extreme events

in coastal areas (Toth and Hizsnyik, 2008).

9.3.4.4. Recreation and Tourism; Forestry

Studies assessing the changes in economic value of recreation and

tourism due to climate change are relatively fewer in number (coastal

tourism is discussed in Section 5.4.4.2). Both sensitivity to climate

variability and climate change have been considered in the literature.

While some studies locate an increase in values for certain regions others

estimate shifts in tourism and losses (Hamilton et al., 2005; Bigano et

al., 2007; Beniston, 2010). Methodological challenges and contrasting

findings for the short and long run pose problems in generalizing findings

(economic values for recreation and tourism are discussed in Section

10.6). Change in economic values will impact rural communities (Lal et

al., 2011), with the linkages between biodiversity, tourism, and rural

livelihoods and rural landscapes being an established one both for

developing and developed countries (Scott et al., 2007; Collins, 2008;

Wolfsegger et al., 2008; Hein et al., 2009; Nyaupane and Poulde, 2011).

It has been argued that climate change would have adverse impacts

on various ecosystems, including forests and biodiversity in many regions

of the world (Preston et al., 2006; Stern, 2007; Eliasch, 2008; ADB,

2009; Ogawa-Onishi et al., 2010; Tran et al., 2010) and these will have

implications for rural livelihoods and economies (Fleischer and Sternberg,

2006; Safranyik and Wilson, 2006; Chopra and Dasgupta, 2008; Kurz et

al., 2008; Walton, 2010). However, monetary valuation of changes in

non-marketed ecosystem services due to climate change continues to

pose a challenge to researchers. To overcome some of the limitations,

multi-criteria analysis has been used for forest management (Fürstenau

et al., 2007).

9.3.5. Key Vulnerabilities and Risks

9.3.5.1. Drivers of Vulnerability and Risk

Discussions on climate vulnerability in rural areas must recognize

competing conceptualizations and terminologies of vulnerability,

particularly those of “starting point” and “end-point” vulnerability

(O’Brien et al., 2007). The focus here is on starting point vulnerability,

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r contextual vulnerability (see Glossary and Chapter 19), while we

consider risk to be the probability of adverse impact resulting from

exposure and vulnerability (see Chapter 19). These distinctions are

important because theycan result in contradictory findings regarding

vulnerability in rural areas, and the policy prescriptions derived therefrom

are also different.

There is low agreement, but medium evidence, on the direction in which

some key factors may affect vulnerability or resilience in rural areas,

including rainfed as opposed to irrigated agriculture, small-scale and

family-managed farms, integration into world markets, and diversification.

Brouwer et al. (2007), contrary to expectations, found that vulnerability

to flooding in Bangladesh in terms of damage suffered was lower for

households that fully depended on natural resources than those who

did not. Osbahr et al. (2008) found that diversification in rural areas

does not always reduce vulnerability and can increase inequity within

communities if it is not accompanied by reciprocity. There is robust

evidence and high agreement on the importance for resilience of drivers

such as access to land and natural resources, flexible local institutions

and knowledge and information, and the association of gender and

vulnerability (see Box CC-GC and Chapter 13).

The most commonly used approaches to analyzing causes of vulnerability

use the concepts of entitlements or livelihoods in evaluating the multi-

scale factors shaping people’s assets, as well as their adaptive capacity

to hazards and stressors. Although vulnerability is experienced locally,

its causes and solutions occur at different social, geographic, and temporal

scales, and are seen as context dependent (Ribot, 2010). Non-climate

factors affecting vulnerability in rural areas at both individual and

community levels (Eakin and Wehbe, 2009) include the following:

• Physical geography, for example, desert or semi-desert conditions

(Lioubimtseva and Henebry, 2009), remoteness (Horton et al., 2010),

level of dependence on climate conditions (Brondizio and Moran,

2008; Sietz et al., 2011)

• Economic constraints and poverty (Macdonald et al., 2009; Mertz

et al., 2009a; Ahmed et al., 2011; Sietz et al., 2011)

• Gender inequalities (Nelson et al., 2002)

• Social, economic, and institutional shocks/trends (e.g., urbanization,

industrialization, prevalence of female-headed households,

landlessness, short-time policy horizons, low literacy, high share of

agriculture in GDP), as well as demographic changes, HIV/AIDS, access

to and availability of food, density of social networks, memories of

past climate variations, knowledge, and long-term residence in the

region (Parks and Roberts, 2006; Brondizio and Moran, 2008; Cooper

et al., 2008; Macdonald et al., 2009; Mertz et al., 2009a; Simelton

et al., 2009; Gbetibouo et al., 2010b; Ruel et al., 2010; Sallu et al.,

2010; Ahmed et al., 2011; Mougou et al., 2011; Seto 2011).

This section focuses on the following drivers of vulnerability to climate

change: water, market orientation and farm scale, institutions and access

to resources, gender, migration, and access to information and knowledge.

9.3.5.1.1. Access to water

Reducing vulnerability requires a reduction of the multiple non-climate-

related pressures on freshwater resources (e.g., water pollution, high

ater withdrawals) together with improvement of water supply and

sanitation in developing countries (Kundzewicz et al., 2008). Water

supply will be adversely affected by climate change, but vulnerability

of populations will also be determined by other elements, such as the

role of institutions in facilitating the access to water, or people’s demand,

which in turn is influenced by local cultural norms (Wutich et al., 2012)

and perceptions of vulnerability which may differ between men and

women (Larson et al., 2011). Improvements in technologies can reduce

the perception of water scarcity and increase water demand without

reductions in underlying vulnerability (El-Sadek, 2010; Sowers et al.,

2011). Where appropriate water management institutions exist and

are effective, their role in improving rural livelihoods has been

demonstrated, for example in Tanzania’s Great Ruaha basin (Kashaigili

et al., 2009).

Past research has tended to agree that rainfed agriculture is more

vulnerable to climate change (Bellon et al., 2011) and that irrigation is

needed to decrease that vulnerability (Gbetibouo et al., 2010a). More

recent findings suggest that this is context dependent and irrigation

has been found to increase vulnerability in certain cases (Eakin, 2005;

Lioubimtseva and Henebry, 2009). Cooper et al. (2008) concluded that in

rainfed sub-Saharan Africa the focus should be on improving productivity

of rainfed agriculture instead of irrigation as irrigation schemes are also

being threatened by drought, and Ahmed et al. (2011) emphasize the

role of drought-tolerant crops.

9.3.5.1.2. Market orientation and farm scale

Some authors argue that opening markets to international trade

increases vulnerability of small farmers and poor people. However,

linkages among international, regional, and local markets are not clear,

including how global prices affect regional and local prices in the long

term (Ulimwengu et al., 2009). Market integration is seen as reducing

the capacity of indigenous or smallholder systems for dealing with

climate risk in Bolivia (Valdivia et al., 2010), Honduras (McSweeney and

Coomes, 2011), Mexico (Eakin, 2005), Mozambique (Eriksen and Silva,

2009; Silva et al., 2010), and in the Sahel (Fraser et al., 2011) by

variously accelerating socioeconomic stratification and reducing crop

diversity. On the other hand, distance from large markets is seen as

increasing vulnerability of rainfed mixed crop/livestock areas in sub-

Saharan Africa (Jones and Thornton, 2009) and the Peruvian Altiplano

(Sietz et al., 2011). Each case needs to be analyzed within its complexity,

considering interactions among all the factors that can affect vulnerability

(Rivera-Ferre et al., 2013a).

Regarding the scale of farms, some authors suggest that small-scale

farming increases the vulnerability of communities in rural areas

(Gbetibouo et al., 2010b; Bellon et al., 2011) although their resilience

(stemming from factors such as indigenous knowledge, family labor,

livelihood diversification) should not be underestimated. Brondizio and

Moran (2008) indicate that small farmers are less vulnerable than large,

monocrop farmers when climatic variations make an area inappropriate

for a particular crop, because they tend to cultivate multiple crops and

work with on-farm biodiversity. However, they recognize that small

farmers tend to suffer from technological limitations, low access to

extension services, and market disadvantages.

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9.3.5.1.3. Institutions, access to resources, and governance

Institutions and networks can affect vulnerability to climate change:

through distribution of climate risks between social groups; by

determining the incentive structures for adaptation responses; and

by mediating external interventions (e.g., finances, knowledge and

information, skills training) into local contexts (Agrawal and Perrin,

2008; Ribot, 2010). Institutions can decrease vulnerability (Anderson et

al., 2010) or increase it (Eakin, 2005). Governance structures and

communication flows as shown in a Swiss mountain region vulnerable

to climate change (Ingold et al., 2010) and the knowledge and perceptions

of decision makers are also important. Romsdahl et al. (2013) show that

local government decision makers in the U.S. Great Plains resist seeing

climate change as within their responsibilities, which has contributed

to low levels of planning for either adaptation or mitigation, and thus

to greater vulnerability, but that a reframing of issues around current

resource management priorities could allow proactive planning.

Lack of access to assets, of which land is an important one, is accepted

to be an important factor increasing vulnerability in rural people

(McSweeney and Coomes, 2011). The breakdown of traditional land tenure

systems increases vulnerability, particularly for those who experience

poorer land access as a result (Brouwer et al., 2007; Dougill et al., 2010;

Fraser et al., 2011). Those who benefit, for example, wealthier farmers

who increased their landholding after privatization in Botswana, remain

less vulnerable (Dougill et al., 2010).

9.3.5.1.4. Migration

The relationship of vulnerability to migration is complex. Areas of

out-migration can experience reduced vulnerability if migrants send

remittances, or increased vulnerability if the burden of work, usually

for women, also increases. The decline in transmission of traditional

knowledge through social networks can also increase vulnerability

(Valdivia et al., 2010). Furthermore, those places receiving migrants can

experience an excessive demographic growth, which increases pressure

over scarce resources, as is being experienced in the semiarid tropics

(Cooper et al., 2008; Obioha, 2008). Brondizio and Moran (2008) found

that in-migration in the Amazon brought people with knowledge that

is ill-adapted to the local environment (see Section 12.4).

9.3.5.1.5. Gender

Box CC-GC sets out the general issues on climate change and gender-

related inequalities. These are of special relevance to rural areas,

particularly but not solely in the developing world (Nelson and Stathers,

2009; Vincent et al., 2010; Alston, 2011) (robust evidence, high

agreement). Access to land shows strong differences between men and

women, as do labor markets (FAO, 2010), and access to non-farm

entrepreneurship (Rijkers and Costa, 2012). Fewer than 20% of the

world’s landholders are women, but women still play a disproportionate

role in agriculture. On average women make up around 43% of the

agricultural labor force in developing countries; in South Asia almost

70% of employed women work in agriculture, and more than 60% in

sub-Saharan Africa (FAO, 2010, 2011). Climate change also increases

ulnerability through male out-migration that increases the work to

women (Chindarkar, 2012); cropping and livestock changes that affect

gender division of labor (Lambrou and Paina, 2006); increased difficulty

in accessing resources (fuelwood and water) (Tandon, 2007); and

increased conflicts over natural resources (Omolo, 2011).

Women are generally, though not in every context, more vulnerable to

the impacts of extreme events, such as floods and tropical cyclones

(Neumayer and Plümper, 2007).

9.3.5.1.6. Knowledge and information

Lack of access to information and knowledge of rural people can also

interact with all the above mentioned drivers to mediate vulnerability.

Shared knowledge and lessons learned from previous climatic stresses

provide vital entry points for social learning and enhanced adaptive

capacity (Tschakert, 2007). But while some authors emphasize the need

for local responses and indigenous knowledge to reduce vulnerability

(Valdivia et al., 2010), and call for an integration of local knowledge

into climate policies (Nyong et al., 2007; Brugger and Crimmins, 2012),

Bellon et al. (2011) state that local knowledge is too local, and in some

contexts gathering information from further away is important.

Access to information alone is not a guarantee of success. Coles and

Scott (2009) found that in Arizona, despite ample access to weather

forecasting, ranchers did not rely on such information, implying that

changes are required to make more attractive information to users, as

well as to understand prevailing local cultures and norms.

It is also important how knowledge is produced, managed, and

disseminated within the formal institutional structure to address

vulnerability issues. A local case study in Sweden shows that limited

cooperation between local sector organizations, lack of local coordination,

and an absence of methods and traditions to build institutional

knowledge present barriers to manage vulnerability (Glaas et al., 2010).

In Benin, as elsewhere in Africa, there is a lack of coordination between

climate policies and the policies and practices that govern agricultural

research and extension, while good practice at project level has been

insufficiently harnessed to foster collective learning of farmers and other

agricultural stakeholders, and thus adaptation to climate change

(Moumouni and Idrissou, 2013a,b). For institutional learning, knowledge

transfer, and more reliable assessments of local vulnerabilities, local

institutional structure must be flexible, establishing communication

mechanisms among public authorities, other knowledge producers, and

civil society (Glaas et al., 2010).

9.3.5.2. Outcomes

The outcome of vulnerability is the result of, and interaction of, the

driving forces that determine vulnerability in a given sector, social group,

and so forth. This section analyzes how different drivers may affect

specific vulnerable groups in rural areas, particularly pastoralists,

mountain farmers, and artisanal fisherfolk. Box 9-2 takes a specific

economic sector important in rural areas and demonstrates the interplay

of vulnerability and exposure.

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Chapter 9 Rural Areas

9.3.5.2.1. Pastoralists

Pastoralists have developed successful strategies for responding to

climate variability, especially “strategic mobility” in pursuit of high-

quality grazing (Krätli et al., 2013), in combination with shorter-term

coping strategies (Morton, 2006), for example, in sub-Saharan Africa

(Davies and Bennett, 2007; Kristjanson etal., 2010) or Inner Mongolia

(Wang and Zhang, 2012). However, mobility, a key component for

community resilience, is declining , increasing the vulnerability of people

in arid and semiarid regions (Lioubimtseva and Henebry, 2009; Fraser

et al., 2011). The lack of other alternatives in certain marginal areas

where animals are the only secure assets can lead to overstocking and

overgrazing, and thus to increased vulnerability of pastoralism (Cooper

et al., 2008).

Box 9-2 | Tourism and Rural Areas

The three major market segments of tourism most liable to be affected by climate change are rural-based, namely, coastal tourism,

nature-based tourism, and winter sports tourism (Scott et al., 2012). Tourism is a signiﬁcant rural land use in many parts of the world,

yet compared to other economic sectors in rural areas, the impacts of climate change are typically under-researched. In the

Caribbean, for example, tourism has overtaken agriculture in terms of economic importance, with several regional states (including

the Bahamas, the Cayman Islands, and St Lucia) receiving more than 60% of their GDP from this industry (Meyer, 2006). Coastal

environments elsewhere in the world are also characterized by dependence on rural tourism, and are known to be vulnerable to

cyclones and sea level rise (Payet, 2007; Klint et al., 2012a).

Terrestrial natural resource-based tourism is also a signiﬁcant foreign exchange earner in many countries. In sub-Saharan Africa,

between 25 and 40% of mammal species in national parks are likely to become endangered by 2080, assuming no species migration

(and 10 to 20% with the opportunity for migration) (Thuiller et al., 2006). There are also many rural environments viewed as “iconic”

or having cultural signiﬁcance that are vulnerable to climate change. In South Africa, for example, the Cape Floral (fynbos) ecosystem

has a high level of species endemism which will be vulnerable to the projected increase in dry conditions (Midgley et al., 2002; Boko

et al., 2007). The projected increase in climate change-related hazards, such as glacial lake outbursts, landslides, debris ﬂows, and

ﬂoods, may affect trekking in the Nepali Himalayas (Nyaupane and Chhetri, 2009).

The development of tourism has, in many cases, increased levels of exposure to climate change impacts. In the Caribbean, for example,

tourism has led to considerable coastal development in the region (Potter, 2000), which may exacerbate vulnerability to sea level rise.

In many cases, the carbon emissions resulting from participating in rural tourism threaten the very survival of the areas being visited.

This is often the case for very remote locations, for example, polar bear tourism in Canada (Dawson et al., 2010), and dive tourism in

Vanuatu (Klint et al., 2012b). Although on aggregate resource consumption of tourists and locals has been shown to be similar in

developed county contexts (e.g., in Italy; Patterson et al., 2007); in many developing countries resource use by tourists is much higher

than that of locals (e.g., in Nepal; Nepal, 2008).

Despite the potential impacts of climate change on rural tourism, there is low evidence of signiﬁcant concern, which impedes adaptive

responses. Surveys in both the upper Norrland area of northern Sweden and New Zealand showed that climate change is not perceived

to pose a major threat in the short term, relative to other business risks perceived by small business owners and tourism operators

(Hall, 2006; Brouder and Landmark, 2011).

That said, there is evidence that, with planned adaptation, tourism can ﬂourish in rural areas under climate change. In the Costa

Brava region of Spain, for example, although the increasing temperatures and reduced water availability are projected to negatively

impact tourism in the current high seasons, there is scope to shift to the current shoulder seasons, namely April, May, September, and

October (Ribas et al., 2010). Recognition of the opportunities for adaptation has also necessitated reassessment of the extent of the

potential impacts of climate change on the tourism industry in rural areas. With the availability of snowmaking as a (costly and

uncertain) adaptation in the eastern North American ski industry, only 4 out of 14 ski areas are at risk before 2029, but 10 out of 14

in the period 2070–2099 (Scott et al., 2006).

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his is “induced vulnerability” (Krätli et al., 2013), arising from a range

of social, economic, environmental, and political pressures external to

pastoralism that bring about encroachment on rangelands; inappropriate

land policy; undermining of pastoral culture and values; and economic

policies promoting uniformity and competition over diversity and

complementarity. Other authors list as constituents of increased

vulnerability: population growth; increased conflict over natural resources;

changed market conditions and access to services under liberalization;

concentration of political power in national centers; and perceptions

that pastoralists are backward (Smucker and Wisner, 2008; Dougill et

al., 2010; Dong et al., 2011; Rivera-Ferre and López-i-Gelats, 2012).

These in turn can be seen as results of what Reynolds et al. (2007)

conceptualize as two key features of dryland populations: remoteness,

and distance from the centers and priorities of decision makers or

“distant voice.” However, Dong et al. (2011) and Sietz et al. (2011) stress

the geographic differentiation of pastoral systems (and more broadly

of dryland systems).

9.3.5.2.2. Mountain farmers

Mountain ecosystems have been identified as extremely vulnerable to

climate change (Fischlin etal., 2007), and thus populations have a high

exposure to climate change. A detailed understanding of climate change

impacts in mountain areas is difficult because of physical inaccessibility

and scarcity of resources for research in mountain states and regions

(Singh et al., 2011), as well as more generic uncertainties relating to

climate projection.

Mountain dwellers, as pastoralists in drylands, are adapted to live in

steep and harsh and variable conditions, and thus have a variety of

strategies to adapt and foster resilience to changing climatic conditions.

However, to develop their strategies they need to overcome other drivers

that can affect their vulnerability in different contexts. For instance, in

most developed countries, mountains are becoming depopulated (Gehrig-

Fasel et al., 2007; Gellrich et al., 2007; López-i-Gelats, 2013) given the

extreme climatic conditions and their remoteness and subsequent

isolation, while in developing countries (e.g., tropical mountain areas)

there is a trend toward increasing population (Huber et al., 2005; Lama

and Devkota, 2009). The impacts of the projected warming on mountain

farming, as well as their adaptation strategies, differ spatially because

the socioeconomic role of mountains varies significantly between

industrialized and industrializing or non-industrialized countries (Nogués-

Bravo et al., 2007). Mountain grasslands in developed countries are

usually managed via a sub-exploitation model that involves the intensive

use of the most productive areas and the abandonment of those regions

where production is economically less viable (López-i-Gelats et al.,

2011). In contrast, mountain grasslands in developing countries remain

centers of fodder and livestock production. Thus, two general trends

are identified in world mountain grasslands: while temperate mountain

grasslands tend to suffer from conversion to agriculture, and land

abandonment where livestock raising is less feasible (Gellrich et al., 2008),

in tropical mountain grasslands the main cause of degradation is

overgrazing, linked to processes of demographic growth. Land

privatization, loss of grazing rights, or changes in land use (e.g.,

development of infrastructure) also affect mountain farmers both in

developed and developing countries (Tyler et al., 2007; Xu et al., 2008).

9.3.5.2.3. Artisanal fisherfolk

Small coastal and riparian rural communities face several drivers that

increase their vulnerability, which remain largely ignored by mainstream

fisheries policy analysts; for example, the potential impact of demographic,

health, and disease trends, or of wider development policy trends (Hall,

2011); pressure from other resources (e.g., water, agriculture, coastal

defense); unbalanced property rights; and lack of adequate health systems,

potable water, or sewage and drainage (Badjeck et al., 2010). The most

important drivers affecting small-scale fisheries can be grouped into

international trade and globalization of markets; technology; climate

and environment; health and disease; demography; and development

patterns and aquaculture. For instance, freshwater fisheries are threatened

by increasing irrigation, while vulnerability of coastal fisheries increases

with mangrove loss to aquaculture facilities in response to growing

markets for prawns (Hall, 2011). Another difficulty faced by fisheries-

based livelihoods is the neglect of governments and researchers, which

is more focused on industrial fishing than artisanal fishing (Mills et al.,

2011).

9.4. Adaptation and Managing Risks

9.4.1. Framing Adaptation

AR4 stated with very high confidence that adaptation to climate change

was already taking place, but on a limited basis, and more so in developed

than developing countries. Since then,the documentation of adaptation

in developing countries has grown (high confidence).Adaptation is

progressive,andis distinguished from coping as it reduces vulnerability in

the case of re-exposure to the same hazard (Vincent et al., 2013):it can

therefore be identified even without high confidence that a local hazard

or climate trend is attributable to global climate change—indeed many

cases of adaptation are driven primarily by other stressors, but have the

result of aiding adaptation to climate change(Berrang-Fordet al., 2011).

Many adaptations do build on examples of responses to past variability

in resource availability, and it has been suggested that the ability to

cope with current climate variability is a prerequisite for adapting to

future change (Cooper et al., 2008). At the same time, however, it cannot

be assumed that past response strategies will be sufficient to deal with

the range of projected climate change. In some cases, existing coping

strategies may increase vulnerability to future climate change, by

prioritizing short-term resource availability (Adepetu and Berthe, 2007;

O’Brien et al., 2007). In Malawi, for example, forest resources are used

for coping (gathering wild food and firewood to sell), but this process

reduces the natural resource base and increases vulnerability to future

flooding through reduced land cover and increased overland flow

(Fisher et al., 2010). In developing countries, there is high confidence

that adaptation could be linked to other development initiatives aiming

for poverty reduction or improvement of rural areas (Eriksen and O’Brien,

2007; Hassan, 2010; Nielsen et al., 2012; see also Section 13.4). For

more information on the integration of adaptation and development in

climate-resilient development pathways, see Chapter 20. In Ethiopia,

for example, “low regrets” measures to respond to current variability

are important to shift the trajectory from disaster-focused to longer-

term vulnerability reduction (Conway and Schipper, 2011).

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9.4.2. Decision Making for Adaptation

Decision making for adaptation takes place at a variety of levels, and

can be public or private. International mechanisms variously support

adaptation decision making at all levels (see Sections 14.4, 15.2). At the

national and local levels, law and policies can enable planned adaptation

(Stuart-Hill and Schulze, 2010). A longer history of evidence for public

policies to support adaptation exists for developed countries, although

increasingly developing countries are also introducing such policies (for

more information, see Section 15.2, Box 25-2 on Australia’s water policy

and management, and Section 26.9.1 on federal adaptation policies in

the USA and Canada). At local levels, some progress toward adaptation

planning has been observed, particularly in developed countries. In

Australia, for example, western Australia, South Australia, and Victoria

have mandatory State planning benchmarks for 2100 (see Box 25-1)

and, in the Great Plains of the USA, some jurisdictions have developed

plans on either climate adaptation or climate mitigation, although so

far fewer than 20% have done so (Romsdahl et al., 2013). At the local

level, many adaptations are examples of private decisions for adaptation,

undertaken by NGOs (primarily in developing countries, often in the

form of community-based adaptation), and companies and individuals.

Public and private decision making for adaptation is not always mutually

exclusive: one example of where policy can support private adaptation

is in the provision of index-based insurance schemes (Linnerooth-Bayer

and Mechler, 2007; Suarez and Linnerooth-Bayer, 2010), which have

variously been trialed in India, Africa, and South America (Patt et al.,

2009, 2010; for a case study on index-based weather insurance in Africa,

see Box 22-1). However, national policies and laws are not always

mutually supportive of private actions (Stringer et al., 2009).

There is now high confidence that public decision making for adaptation

can be strengthened by understanding the decision making of rural people

in context, and in particular considering examples of autonomous

adaptation and the interplay between informal and formal institutions

(Bryan et al., 2009; Eakin and Patt, 2011; Adhikari and Taylor, 2012; Naess,

2012). Adaptation can also build upon local and indigenous knowledge

for responding to weather events and a changing climate as has been

observed in Samoa (Lefale, 2010; see Chapter 29), the Solomon Islands

(Rasmussen et al., 2009; see Chapter 29), Namibia (Newsham and Thomas,

2011), Canada (Nakashima et al., 2011; see Chapter 24), the Indo-Gangetic

Plains (Rivera-Ferre et al., 2013b), and Australia (Green et al., 2010).

9.4.3. Practical Experiences of Adaptation in Rural Areas

In AR4, examples of adaptation in rural areas exhibited a bias toward

developed countries (WGII AR4 Chapter 17), but since then practical

examples of adaptation in rural areas have increased substantially in

developing countries (very high confidence). These practical experiences

of adaptation are found in agriculture, water, forestry and biodiversity,

and fisheries.

9.4.3.1. Agriculture

Agricultural societies have a history of responding to the impacts of

change in exogenous factors, including (but not limited to) weather and

limate (Mertz et al., 2009a). They undertake a range of adjustment

measures relating to their farming practices—for example, planting,

harvesting, and watering/fertilizing existing crops; using different

varieties; diversifying crops; and implementing management practices

such as shading and conservation agriculture. Table 9-7 gives some

examples; Box 9-3 describes adaptation initiatives in the beverage crop

sector. More information on agricultural adaptation is available in

Sections 23.8.2 (Europe), 24.4.3.5 (Asia), 25.7.2 (Australasia), 26.5.4

(North America), and 27.3.4.2 (Central and South America).

Conservation agriculture shows promising results and can be used as

an adaptation (Speranza, 2013) and for sustainable intensification of

production (Pretty et al., 2011), with significant yield productions

observed in South Asia and southern Africa (Erenstein et al., 2012). See

Box 22-2 for a case study on integrating trees into annual cropping

systems. Water management for agriculture is also critical in rural areas

under climate change, for example, the use of rainwater harvesting

(Vohland and Barry, 2009; Kahinda et al., 2010; Rivera-Ferre et al.,

2013b), and more efficient irrigation, particularly in rural drylands

(Thomas, 2008).

Adaptations are also evident among small-scale livestock farmers

(Kabubo-Mariara, 2008, 2009; Rivera-Ferre and López-i-Gelats, 2012),

who use many different strategies, including changing herd size and

composition, grazing and feeding patterns, or diversifying their livelihoods;

also they may use new varieties of fodder crops suited to the changing

conditions (Salema et al., 2010).

Diversified farms are more resilient than specialized ones (Seo, 2010);

but rural societies also diversify their income sources beyond agriculture,

which in many contexts allows them to reduce their risk exposure.

Examples include the exploitation of gums and resins in Kenya

(Gachathi and Eriksen, 2011).There may be some rural areas, however,

where limits to agricultural adaptation are reached, and thus the only

option that remains is to migrate or diversify away from farming (Mertz

et al., 2011). According to Chapter 7, adaptation leads to lower

reductions in food production with more effective adaptation (of around

15 to 20% compared with no adaptation), and adaptations are more

successful at higher latitudes (for maize, wheat, and rice) than in tropical

regions. Figure 7-8 shows the varying efficiency of different crop

adaptation measures, with cultivar adjustment leading to the largest

percentage difference from the baseline, compared with irrigation

optimization and planting date adjustment (although this shows the

largest variation).

9.4.3.2. Water

As well as being an important input to agriculture, adaptation in water

resources through improved management is critical in rural areas, not

only at basin level but also for human settlements (Mukheibir, 2008).

The extent to which adaptation measures have been implemented

to date varies: in a study from Europe, Africa, and Asia, European

basins were most advanced (Krysanova et al., 2010). In the cases of

transboundary basins additional barriers exist to adaptive management

measures, particularly in Africa (Goulden et al., 2009), although

examination of potential institutional designs has been undertaken

639

Rural Areas Chapter 9

Continued next page

Agricultural adaptations Examples Where observed Source

odifying planting, harvesting,

and fertilizing practices for

rops

aize and wheat crops Central and South America (Bolivia, Argentina, Chile); South Africa

(including North West, Limpopo, and KwaZulu-Natal provinces)

NCC (2007), Thomas et al. (2007), Magrin et al.

(2009), Meza and Silva (2009)

omposting and coralling of

livestock to collect waste

frica (South Africa, including North West, Limpopo, and KwaZulu-

Natal provinces; northern Burkina Faso; Sahelian region of Mali)

depetu and Berthe (2007), Thomas et al.

(2007), Barbier et al. (2009), Bryan et al. (2009)

hanging amount or area of

and under cultivation

outh Africa Bryan et al. (2009)

oving winter wheat northwards China Lin et al. (2005)

Expansion of ﬁ elds Northern Burkina Faso Barbier et al. (2009)

ncrease in the size of plots Sahelian region of Mali Adepetu and Berthe (2007)

Using different varieties (e.g.,

early maturing, drought-

esistant)

Early maturing cultivars South Brazil Walter et al. (2010)

North America Coles and Scott (2009)

rought-tolerant cultivars Asia Thomas (2008), Zhao et al. (2010)

South Africa and Ethiopia Bryan et al. (2009)

Ghana Gyampoh et al. (2008)

orthern Burkina Faso Barbier et al. (2009)

Sahelian region of Mali and Nigeria Adepetu and Berthe (2007)

North West, Limpopo, and KwaZulu-Natal provinces of South Africa Thomas et al. (2007)

iversifying crops and/or

animal species

rops Peruvian Andes Lin (2011)

South America Montenegro and Ragrab (2010)

ortheastern Mexico Eakin and Appendini (2008), Eakin and

Bojorquez-Tapia (2008)

Tasmania, Australia Smart (2010)

KwaZulu-Natal, South Africa Thomas et al. (2007)

Replacing cattle with hardier

goats and camels

Kenya Rivera-Ferre and López-i-Gelats (2012)

Commercialization of

agriculture

Ghana Gyampoh et al. (2008)

Limpopo Province, South Africa Thomas et al. (2007)

Income generation from natural

resources (e.g., fuelwood)

Limpopo River Basin, Botswana Dube and Sekhela (2007)

Water control mechanisms

(including irrigation and water

allocation rights)

Improved rice harvests Monsoonal Asia Hatcho et al. (2010)

Adaptation for quinoa Bolivian Altiplano Geerts and Raes (2009)

Adaptation for tomatoes Central Brazil

Adaptation for cotton Northern Argentina

Adaptation for rice Northeast China Lin et al. (2005)

Small water harvesting pits in

improved yields and incomes due

to improved soil moisture

Ethiopia Bryan et al. (2009), Amede et al. (2011)

Burkina Faso Barbier et al. (2009), Hertsgaard (2011)

South Africa Bryan et al. (2009)

Ghana Gyampoh et al. (2008)

Dry season vegetable production

through irrigation to enable two

crop cycles

Northern Burkina Faso Barbier et al. (2009)

Sahelian region of Mali and Nigeria Adepetu and Berthe (2007)

Limpopo Province, South Africa Thomas et al. (2007)

Shading and wind breaks For coffee Brazil, Costa Rica, and Colombia Camargo (2010)

Ethiopia Bryan et al. (2009)

Conservation agriculture (e.g.,

soil protection, agroforestry)

Honduras, Nicaragua, and Guatemala Holt-Gimenez (2002)

Burkina Faso Barbier et al. (2009), Hertsgaard (2011)

Ethiopia Bryan et al. (2009)

Sahelian region of Mali Adepetu and Berthe (2007)

Table 9-7 | Examples of adaptations in the agricultural sector in different regions.

640

Chapter 9 Rural Areas

(Huntjens et al., 2012). In the Middle East and North Africa, while

supply-side measures are advanced, little attention has been paid to

the demand-side measures that will be critical in a changing climate

(Sowers et al., 2011).

While the majority of focus on adaptation concerning water relates to

its availability, many rural areas in both developed and developing

countries are subject to riverine or coastal flooding. In the low-lying

Netherlands protection measures have been employed, including

increasing river runoff, increasing storage for water (Deltacommissie,

2008; Kabat et al., 2009), and small-scale containment of flood risks

through increasing compartmentalization (Klijn et al., 2009). In the

Mekong Delta in Vietnam, the government’s “living with floods” program

has encouraged rice farmers to shift to aquaculture, while the planned

relocation of 20,000 “landless and poor households” has altered social

networks and livelihoods (De Sherbinin et al., 2011). See Table 9-8 for

further examples.

More information on adaptation in the water sector is available in

Sections 24.4.1.5 and 24.4.2.5 (Asia), 26.3.3 (North America), and

27.3.1.2 and 27.3.2.2 (Central and South America).

9.4.3.3. Forestry and Biodiversity

Effective management is also essential for adaptation of forests and

biodiversity to climate change, particularly involving (where appropriate)

communities (Porter-Bolland et al., 2012). Forest resources have been

shown to play a role in enabling livelihood adaptation during extreme

events in Zambia, Mali, and Tanzania, although it should take place

Agricultural adaptations Examples Where observed Source

odifying grazing patterns

or herds

tilizing spatial variability in

esources

rctic Bartsch et al. (2010)

ast Africa Eriksen and Lind (2009)

Southern Africa O’Farrell et al. (2009)

orthern Burkina Faso Barbier et al. (2009)

Sahelian region of Mali and Nigeria Adepetu and Berthe (2007)

North West, Limpopo, and KwaZulu-Natal provinces, South Africa Thomas et al. (2007)

roviding supplemental

feeding for herds/storage of

nimal feed

rctic Forbes and Kumpula (2009)

South Africa Bryan et al. (2009)

se of sorghum and hay residue

for feeding livestock

orthern Burkina Faso Barbier et al. (2009)

Sahelian region of Mali and Nigeria Adepetu and Berthe (2007)

Cutting fodder for livestock Limpopo Province, South Africa Thomas et al. (2007)

nsuring optimal herd size Changing size of European

reindeer herds to match pasture

vailability

orthern areas of Norway, Sweden, Finland, and Russia Rees et al. (2008)

Culling of livestock Northern Nigeria Adepetu and Berthe (2007)

Selling of livestock Northern Burkina Faso Barbier et al. (2009)

ahelian region of Mali and Nigeria Adepetu and Berthe (2007)

Developing new crop and

livestock varieties

Biotechnology and breeding Brazil and Argentina Urcola et al. (2010), Marshall (2012)

Northern Nigeria Adepetu and Berthe (2007)

Table 9-7 (continued)

Type Example Where it has been observed and source

Supply-side

mechanisms

Dams Proposed in the Volta River in Ghana (van de Giesen et al., 2010)

Reservoirs Asia (Tyler and Fajber, 2009), particularly in areas where water stress is an issue of distribution rather than absolute

shortage (Biemans et al., 2011; Rivera-Ferre et al. 2013)

Groundwater pumping Arid and semi-arid South America (Döll, 2009; Kundzewicz and Döll, 2009; Zagonari, 2010; Burte et al., 2011)

Groundwater recharge Potential identiﬁ ed in India (Sukhija, 2008)

Irrigation (often using water-saving technology) Asia (Ngoundo et al., 2007; Tischbein et al., 2011)

Fog interception practices South America (Holder, 2006; Klemm et al., 2012)

Water capture Bolivia (PNCC, 2007)

Demand-side

mechanisms

Improved management, e.g., through efﬁ ciency Asia (Kranz et al., 2010), South America (Geerts et al., 2010; Montenegro and Ragab, 2010; Van Oel et al., 2010; Bell et al.,

2011); Argentine Pampas (Quiroga and Gaggioli, 2010)

Policies Murray-Darling Basin Authority (MDBA) established to address over-allocation of water resources (Connell and Grafton,

2011; MDBA, 2011). See also Box 25-3 on Australia’s water policies.

Reviewing allocation rights Indogangetic Plains (Rivera-Ferre et al., 2013b); Australia’s MDBA reviewed the “exceptional circumstances” concept in

drought policy (Productivity Commission, 2009)

Table 9-8 | Examples of adaptations in the water sector observed in different regions.

641

Rural Areas Chapter 9

within a managed context to ensure sustainability (Robledo et al., 2011).

As with water resources, forests can adapt through management of

forest fires, silvicultural practices, and the conservation of forest genetic

resources. Ecological restoration, where required, is another effective

adaptation measure that enhances biodiversity and environmental

services (Benayas et al., 2009), increases the potential for carbon

sequestration, and promotes economic livelihoods in rural areas (Chazdon,

2008), as seen in examples of the Brazilian Atlantic Forest (Calmon et

al., 2011; Rodrigues et al., 2011). Direct species management is important

(Mawdsley et al., 2009). In terms of managing protected areas, to

maintain appropriate habitats a network approach may be effective

(Hole et al., 2011).

As the climate changes, part of adaptive management may entail

modification of existing biodiversity management practices. Manipulating

vegetation composition and stand structure, for example, has been

proposed as an adaptation option to wildfires in Canada (Girardin et

al., 2013; Terrier et al., 2013); for more information on wildfires see Box

26-2. In Central and South America, protected areas of restricted use

reduced fire substantially, but multi-use protected areas are even more

effective; and in indigenous reserves the incidence of forest fire was

reduced by 16% as compared to non-protected areas (Nelson and

Chomitz, 2011).

Reflecting the growing evidence for community-based management

and wise use, an emerging mechanism for ecosystem-based adaptation

includes payment for ecosystem services (PES) (Montagnini and Finney,

2011). The PES literature is more developed for carbon payments, CDM

and REDD+, but some research suggests potential for adaptation as

well (see Section 13.3.1.2 for an assessment of the relationship between

REDD+ and poverty alleviation). Particularly developed in Central and

South America (see Table 27-7 for examples of PES schemes), communities

can be paid for collecting scientific data to contribute to research and

monitoring protocols (Luzar et al., 2011), or for actively managing

natural resources, which may improve adaptive capacity in the longer

term, bearing in mind with reforestation there is a time delay before

payments are received (Locatelli et al., 2008). More indirectly, there are

opportunities for PES to contribute to adaptation indirectly through

Box 9-3 | Adaptation Initiatives in the Beverage Crop Sector

One of the leading initiatives to prepare small-holder producers of beverage crops for adaptation to climate change is the AdapCC

project, which worked with coffee and tea producers in Latin America and East Africa (Schepp, 2010). This process used risk and

opportunity analysis and participatory capacity building (CafeDirect/GTZ, 2010) to help farmers identify changes in management

practices to both mitigate their contribution to climate change and adapt to the changes in climate they perceived to be occurring. In

general the actions for adaptation were a reinforcement of principles of sustainable production, such as using tree shade. Facilitating

processes of adaptation in the context of strong variability in vulnerability between different communities in the same region and

even families within the same community (Baca Gómez, 2010) will be a challenge, but supports the need for participatory community

adaptation processes that would enable families to implement strategies appropriate to their own circumstances and capacity.

Policy recommendations to support adaptation in these sectors (Schroth et al., 2009; Laderach et al., 2010; Schepp, 2010; Eakin et al.,

2011) have prioritized the following interventions to support adaptation:

• Community-based analysis of climate risks and opportunities as a basis for community adaptation strategies

• Improved recording and access to climate information including medium- and long-term predictions

• Sustainable production techniques including soil and water conservation, shaded production systems, diversiﬁcation of

production systems

• Development of new varieties with broader adaptability to climate variation, higher temperatures, and increased drought tolerance

• Financial support to invest in adaptation and reduce risks through climate insurance

• Organization of small producers to improve access to knowledge and ﬁnancial support, and to coordinate implementation

• Environmental service payments and access to carbon markets to support sustainable practices

• Development of value chain strategies across all actors to support adaptation and increase resilience across the sectors.

There are possibilities for synergy between adaptation and mitigation. The sustainability standards Rainforest Alliance and Common

Code for the Coffee Community are piloting climate-friendly standards for producers that aim to reduce the greenhouse gas emissions

from agricultural practices and to increase sequestration of carbon in soils and trees, but also to prepare producers for adapting to

climate change (Linne, 2011; SAN, 2011). The latter consists of improved understanding of climate impacts and promoting sustainable

production practices to increase resilience in the production systems.

642

Chapter 9 Rural Areas

atural adaptation co-benefits (e.g., water regulation and soil protection

for reduced climate impacts in watersheds) (Pramova et al., 2012) and

through the creation of institutional structures that may support adaptive

capacity (Wertz-Kanounnikof et al., 2012). For further case studies on

ecosystem-based adaptation, see Figure 22-8 (Africa), Box CC-EA, and

Section 14.3.2; and for a diagrammatic representation see Figure

CC-EA-1. More information on adaptation for forestry and biodiversity

is available in Sections 23.8.2 and 23.8.4 (Europe), 24.5.1 (Asia), and

25.7.1.2 (Australasia).

9.4.3.4. Fisheries

Adaptation in marine ecosystems is also of relevance to rural areas. As

with terrestrial natural resources, evidence from the marine resources

sphere shows that a transformative approach to fisheries co-management,

introducing ecosystem rights, and participation principles is essential

for adaptation (Andrew and Evans, 2011; Charles, 2011). Such an

approach, involving local fishermen and allowing limited extraction

of resources, favors a balance between resource conservation and

livelihoods, for example, in Brazil (Francini-Filho and Moura, 2008), and

the improvement of livelihoods, as well as the cultural survival of

traditional populations (Moura et al., 2009; Hastings, 2011) (see also

Section 30.6.2.1). Selective use of fishing gear is a recommended

management measure, based on 15 global sites, to ensure sustainable

harvesting of remaining fish stocks (Cinner et al., 2009). According to

Section 6.4.1.1, appropriate management will have a greater impact

on biological and economic conditions than climate change. Table

30-2 outlines potential adaptation options and supporting policies for

fisheries and aquaculture in the Pacific Islands considering a variety of

time scales. Section 7.5 gives additional examples on adaptation for

aquaculture.

9.4.4. Limits and Constraints to Rural Adaptation

The Fourth Assessment Report stated with very high confidence that

there are substantial limits and barriers to adaptation (Adger et al.,

2007). Limits are typically defined (Dow et al., 2013) as hard, that is,

they will not change over time, and are particularly applicable to

biophysical systems (where, e.g., there are critical thresholds to species

and ecosystem tolerances of climate parameters and regimes).

onstraints, on the other hand, are typically soft, and are more relevant

to social systems, where changes in factors such as financial and physical

resources, technology and infrastructure, knowledge and information,

and human resources may change over time. For further information,

see Figure 16-1 and Sections 16.3.2 and 16.4.1. Here we focus on the

soft constraints in social systems that act as barriers to implementation

of practical adaptation options in rural areas.

As with risks and vulnerabilities, the literature emphasizes constraints

to adaptation in rural areas in developing regions, although adaptation

bottlenecks exist also in developed countries (where there has been an

increase in awareness and planning for adaptation, but that has not

necessarily translated into implementation; see Chapter 14). Constraints

to adaptation in developed regions have been observed in North America

(Section 26.8.4.2) and Australasia (Section 25.4.2; Boxes 25-1, 25-2,

25-9). Another key bottleneck comes from the fact that the need for

adaptation to climate change is not the only pressing issue in rural areas

in developed countries (Kiem and Austin, 2013).

There is very high confidence that lack of financial resources (in the

form of credit) and physical resources (such as water and land) are

major factors inhibiting adaptation for farmers in Africa and Asia (e.g.,

Hassan and Nhemachena, 2008; Bryan et al., 2009; Deressa et al., 2009;

Ringler, 2010). A multinomial logit analysis of climate adaptation responses

suggested that access to water, credit, extension services, and off-farm

income and employment opportunities, tenure security, farmers’ asset

base, and farming experience are key to enhancing farmers’ adaptive

capacity (Gbetibouo et al., 2010).

Rural households’ lack of access to technologies and infrastructure (e.g.,

markets) is also a major barrier to adaptation for certain production

systems (medium evidence, high agreement). According to a study of

adoption of improved, high yield maize in Zambia, production and price

risks could render input use unprofitable and prevent rural households

from benefiting from technological change crucial for adaptation

(Langyintuo and Mungoma, 2008). The severe 1997 drought in the

Central Plateau of Burkina Faso highlighted that households with a larger

resources base took advantage of distress sales and high prices of

agricultural commodities (Roncoli et al., 2001). A nationally representative

rural household survey in Mozambique from 2005 shows that, overall,

using an improved technology (improved maize seeds, improved

granaries, tractor mechanization, and animal traction) did not have a

Frequently Asked Questions

FAQ 9.3 | What will be the major ways in which rural people adapt to climate change?

Rural people will in some cases adapt to climate change using their own knowledge, resources, and networks. In

other cases governments and other outside actors will have to assist rural people, or plan and execute adaptation

on a scale that individual rural households and communities cannot. Examples of rural adaptations will include

modifying farming and ﬁshing practices; introducing new species, varieties, and production techniques; managing

water in different ways; diversifying livelihoods; modifying infrastructure; and using or establishing risk-sharing

mechanisms, both formal and informal. Adaptation will also include changes in institutional and governance structures

for rural areas.

643

Rural Areas Chapter 9

statistically significant impact on household income. However when

distinguishing between households using improved technologies,

especially improved maize seeds and tractors, and those who do not,

households that had better market access had significantly higher

income (Cunguara and Darnhofer, 2011). A multinomial choice model

fitted to data from a cross-sectional survey of more than 8000 farms

from 11 African countries showed that better access to markets, extension

and credit services, technology, and farm assets (labor, land, and capital)

are critical for helping African farmers adapt to climate change. Hence

education, markets, credit, and information about adaptation to climate

change, including technological and institutional methods, are important

(Hassan and Nhemachena, 2008).

Although access to credit, water, technologies, and markets are barriers,

more fundamental is access to knowledge and information (very high

confidence). Because adaptation strategies involve dealing with

uncertainty, whether stakeholders have access to information for decision

making and how they perceive and utilize this information affects their

adaptation choices (Dockerty et al., 2006; Sheate et al., 2008; Patt and

Schröter, 2008; Bryan et al., 2009; Deressa et al., 2009; Ringer, 2010).

Relevant information includes that on agricultural technologies that can

be used in adaptation, but in developing countries agricultural research

and extension systems are not integrated with climate planning to deliver

this, as discussed by Moumouni and Idrissou (2013a) for Benin. There is

now an important literature on dissemination of short-term or seasonal

weather forecasts to farmers in developing countries (see Box 9-4).

Access to information is affected by human resources, or social

characteristics (medium evidence, high agreement). These include culture,

gender, age, governance, and institutions (Deressa et al., 2009; Goulden

et al., 2009; Nielsen and Reenberg, 2010; Jones and Boyd, 2011). A

growing body of literature investigates the socio-cognitive, psychological,

and cultural barriers to adaptation. Section 2.2.1.2 explains how culture

and psychology affect decision making; Section 16.2 also discusses how

the framing of adaptation depends on perception of risk and values. For

planned adaptation to be successful, or autonomous adaptation to

occur, actors need to be convinced of the magnitude of risks of climate

change (Patt and Schröter, 2008).

9.5. Key Conclusions and Research Gaps

9.5.1. Key Conclusions

This chapter has assessed impacts of climate change, vulnerability to

climate change, and prospects for adaptation to climate change in the

Box 9-4 | Factors Inﬂuencing Uptake and Utility of Climate Forecasts in Rural Africa

The IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX)

identiﬁed the use of forecasts as a risk management measure (IPCC, 2012). So far the uptake of weather and climate information has

been suboptimal (Vogel and O’Brien, 2006). In Africa annual climate information (e.g., seasonal forecasts) is more used than climate

change scenarios for agricultural development (Ziervogel and Zermoglio, 2009), although attempts to use longer-term climate

projections for crop forecasting and livestock farming have been examined (Boone et al., 2004; Challinor, 2009). The potential for

improved prediction and effective timely dissemination of such information has been noted in different sectors, including water

managers (Ziervogel et al., 2010a) and disaster planners (Tall et al., 2012), as well as farmers (both arable and pastoral) (Klopper et

al., 2006; Archer et al., 2007; Bryan et al., 2009).

Extensive research has taken place to assess factors inﬂuencing uptake and utility of climate forecasts, including mapping of

dissemination through stakeholder networks (Ziervogel and Downing, 2004), and user needs (Ziervogel, 2004). Such studies have

shown that various factors affect dissemination and use, including stakeholder involvement in the process (usually higher when

participatory processes had taken place) (Roncoli et al., 2009; Peterson et al., 2010); effects of user wealth, risk aversion, and

presentational parameters, such as the position of forecast parameter categories, and the size of probability categories (Millner and

Washington, 2011); and the legitimacy, salience, access, understanding, and capacity to respond (Hansen et al., 2011). Gender

differences have been observed in preferred dissemination channels (Archer, 2003; Naab and Korenteng, 2012).

There are promising signs for the integration of scientiﬁc-based seasonal forecasts with indigenous knowledge systems (Speranza et

al., 2010; Ziervogel et al., 2010b). Ensuring improved validity and utility of seasonal forecasts will require collaboration of researchers,

data providers, policy developers, and extension workers (Coe and Stern, 2011), as well as with end users. Additional opportunities to

beneﬁt rural communities come from expanding the use of seasonal forecast information for coordinating input and credit supply,

food crisis management, trade, and agricultural insurance (Hansen et al., 2011). For more information on climate information and

services, and the history, politics, and practice of this area, see Section 2.4.1.

644

Chapter 9 Rural Areas

ural areas of the world. Rural areas are distinctive and important in

the context of climate change because:

• They account for nearly half of the world’s population, even with

rapid urbanization.

• They account for well over half of the world’s poor and extremely

poor people.

• Economic activity and livelihoods in rural areas are closely linked

to natural resources and thus particularly sensitive to climate

variability and climate change.

• Conversely, it is in rural areas that long-established adaptations to

climate variability exist and can form a basis under certain conditions

for adaptations to climate change.

Rural areas are hard to define—there is no internationally valid definition,

and definitions that do exist depend on definitions of the urban (see Table

9-1). They are also extremely diverse, existing in nearly every country of

the world, across low-, middle-, and high-income countries, although

90% of the world’s rural population lives in low- and middle-income

countries, which receive particular attention in this chapter. Rural

areas are undergoing important and rapid changes in terms of their

demography, economic profile, and governance (see Table 9-3)—some

specific to developing countries, some to high-income countries, and

some generic. Many of these changes are in the direction of economic

and livelihood diversification away from agriculture and natural

resources. Others are in the direction of increased rural-urban

interdependencies and less well-defined boundaries between the rural

and the urban.

Many of the non-climate factors characterizing rural areas and populations

within them, especially in low- and middle-income countries, are cited as

factors increasing vulnerability to climate change. There is high agreement

on the importance for resilience of access to land and natural resources,

flexible local institutions, and knowledge and information, and the

association of gender inequalities with vulnerability. There are low levels

of agreement on some of the key factors associated with vulnerability

or resilience in rural areas, including rainfed as opposed to irrigated

agriculture, small-scale and family-managed farms, and integration into

world markets. Specific livelihood niches such as pastoralism and

artisanal fisheries are vulnerable and at high risk of adverse impacts

(high confidence), partly due to neglect, misunderstanding, or

inappropriate policy toward them on the part of governments (Section

9.3.5).

Against this background, discussion of impacts of climate change will

be complex. The impacts of climate change on patterns of settlement,

livelihoods, and incomes in rural areas will be the result of multi-step

causal chains of impact, starting either with increased frequency of

extreme events or with more gradual manifestations of climate change,

and working through impacts on agriculture, ecosystems, or infrastructure.

This increases the uncertainty associated with any particular projected

impact. Biophysical impacts on food production are discussed in

Chapter 7: this is supplemented here by an assessment of impacts on

the production of non-food crops on which many millions of rural

people depend, illustrated in particular by coffee, tea, and cocoa (Box

9-1). Literature on the downstream impacts on incomes and livelihoods

of changes in agricultural production (including livestock and fisheries)

is also assessed.

espite methodological problems in attribution, around the difficulties

of attributing extreme events to climate change, the status of local

knowledge, and the action of non-climate shocks and trends, evidence

for observed impacts, both of extreme events and other categories, is

increasing. Impacts on income and livelihoods can be inferred from

biophysical impacts, but with low confidence. There is high confidence

in geographically specific impacts such as glacier melt in the Andes

(Section 9.3.2).

Major impacts of climate change in rural areas will be felt through

impacts on agricultural production and therefore through agricultural

incomes. In some regions shifts in agricultural production, of food and

non-food crops, are likely to take place, not only as a result of changes

in temperature and rainfall, but also through changes in availability of

irrigation water, which are not necessarily factored into crop yield

projections based on crop models (Section 9.3.3.1). There are also likely

to be impacts on rural infrastructure both in developing and developed

countries (Section 9.3.3.2).

The interconnections between rural and urban areas will be affected in

complex ways. Climate change will impact international trade volumes

in both volume and value terms (limited evidence, medium agreement).

Options exist for adaptations within international agricultural trade

(medium confidence) to reduce market volatility and manage food supply

shortages caused by climate change. Migration patterns will be driven

by multiple factors of which climate change is only one (high confidence)

and establishment of a relation between climate change and intra-rural

and rural-to-urban migration, observed or projected, remains a major

challenge (Section 9.3.3.3).

Climate policies, such as increasing energy supply from renewable

resources, encouraging cultivation of biofuels, or payments under REDD,

will have significant secondary impacts, both positive (increasing

employment opportunities) and negative (landscape changes, increasing

conflicts for scarce resources), in some rural areas (medium confidence).

These secondary impacts, and trade-offs between mitigation and

adaptation in rural areas, have implications for governance, including

the need to promote participation of rural stakeholders (Section 9.3.3.4).

Most studies on valuation highlight that climate change impacts will be

significant especially for the developing regions, due to their economic

dependence on agriculture and natural resources, low adaptive capacities,

and geographical locations (very high confidence). In rural areas especially,

valuation of climate impacts needs to draw upon both monetary and

non-monetary indicators.The valuation of non-marketed ecosystem

services and the limitations of economic valuation models that

aggregate across multiple contexts pose challenges for valuing impacts

in rural areas and require interdisciplinarity and innovative approaches

(Section 9.3.4).

There is a growing body of literature on successful adaptation in rural

areas and constraints upon it, including both documentation of practical

experience and discussion of preconditions (Section 9.3.4). In developing

countries adaptation can be linked to other development initiatives

aiming for poverty reduction or improvement of rural areas, and “low

regrets” measures to respond to current variability can shift the trajectory

from disaster-focused to longer-term vulnerability reduction. Prevailing

645

Rural Areas Chapter 9

onstraints, such as low levels of educational attainment, environmental

degradation, gender inequalities, and isolation from decision making,

create additional vulnerabilities which undermine rural societies’ ability

to cope with climate risks (high confidence). The supply of information

and opportunities for learning will be a key issue.

.5.2. Research Gaps

There is a major continuing need for research on climate change in rural

areas, which takes in their nature as areas with shifting combinations

of human activity, in which agriculture (food crops, non-food crops, and

livestock) is important but not necessarily predominant. Such research

will need to be developed, and extended to rural areas and diverse

categories of rural people throughout the world.

Integrated research is needed on changes in land use and trade-offs

between land uses under climate change, including non-agricultural land

uses such as conservation and tourism. It should examine the trade-

offs and synergies between adaptation and mitigation in rural areas,

the impact of climate policies on rural livelihoods, and the appropriate

structures for governance of natural resources at a landscape level for

both developed and developing countries.

Research is required on the valuation and costing of climate change

impacts, which takes note of the complexity and specificity of rural

areas, with special emphasis on non-marketed ecosystem services and

specific populations that have not as yet been studied.

More research is needed on vulnerability, to identify the most vulnerable

areas, populations, and social categories, but it should include research

on methodological questions such as conceptualizations of vulnerability,

assessment tools, spatial scales for analysis, and the relations between

short-term support for adaptation, policy contexts and development

trajectories, and long-term resilience or vulnerability.

A relevant area will be that of improving understanding of rural-urban

linkages, their evolution, and their management under climate change,

including the respective roles of climate and other factors in rural-urban

migration.

Research is needed on practical adaptation options, not only for agriculture

but also for non-agricultural livelihoods. Adaptation research must also

look at adaptations to institutions, to better enable them to address

lack of access to credit, markets, information, risk-sharing tools, and

property rights. Research must be open to participatory and action-

research approaches that build on both local and scientific knowledge,

and foster learning for adaptation and resilience among rural people.

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