Editing IPCC:AR6/WGII/Chapter-9 (section)

=== 9.11.5 COVID-19 Recovery Stimulus Packages for Climate Action ===

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The COVID-19 pandemic recovery effort includes significant opportunities for African countries to reduce future vulnerability to compound climate, economic and health risks. Fiscal recovery packages could set economies on a pathway towards net-zero emissions, reducing future climate risk or entrench fossil-fuel intensive systems, exacerbating risk ( [[#Hepburn--2020|Hepburn et al., 2020]] ; [[#Dibley--2021|Dibley et al., 2021]] ; [[#IEA--2021|IEA, 2021]] ). Investments in renewable energy, building efficiency retrofits, education and training, natural capital (i.e., ecosystem restoration and EbA), R&amp;D, connectivity infrastructure and sustainable agriculture can help meet both economic recovery and climate goals ( [[#Hepburn--2020|Hepburn et al., 2020]] ; [[#Dibley--2021|Dibley et al., 2021]] ).

The impacts of the COVID-19 pandemic have been substantially worsened by climate hazards in many places. In others, outbreak response has been disrupted ( [[#Phillips--2020|Phillips et al., 2020]] ; [[#Kruczkiewicz--2021|Kruczkiewicz et al., 2021]] ). These vulnerabilities are rooted in insufficient disaster preparedness infrastructure but are almost always worsened by social and economic inequality. Ensuring the most vulnerable populations are properly protected from climate change has co-benefits for recovery from the COVID-19 pandemic ( [[#Manzanedo--2020|Manzanedo and Manning, 2020]] ). In particular, efforts to reduce syndemic vulnerabilities across key sectors (especially health, livelihoods and food security) will lessen climate change impacts and will also reduce the risk and impacts of future epidemics and pandemics, for example, during the pandemic, water scarcity has been a barrier to a key risk mitigation behaviour (hand washing). In the long-term, development efforts focused on WASH will reduce this vulnerability and also reduce the health toll of diarrheal disease linked to climate change ( [[#Anim--2020|Anim and Ofori-Asenso, 2020]] ; [[#Zvobgo--2020|Zvobgo and Do, 2020]] ). Spending recovery funds on social safety nets will reduce inequality and protect the most vulnerable communities (especially women and low-income and marginalised communities) from the social and economic impacts of disasters. Key among these safety nets is universal health coverage, including low- or no-cost access to essential medicine, high-quality preventative care, financial protections against medical debt and increased geographic and population coverage for all services ( [[#Hallegatte--2016|Hallegatte et al., 2016]] ). All of these are key components of climate change adaptation for health and will reduce both the rate at which future outbreaks start and their total scope and impact ( [[#Carlson--2021|Carlson et al., 2021]] ). The co-benefits of multilateral cooperation on the attainment of universal health coverage will be a key determinant of success or failure in both climate change adaptation and pandemic preparedness.

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'''Box 9.8 | Climate change, migration and displacement in Africa'''
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Climatic conditions are important drivers of migration and displacement with migration responses to climate hazards strongly influenced by economic, social, political and demographic processes (Cross-Chapter Box MIGRATE in Chapter 7).

Most climate-related migration and displacement observed currently is within countries or between neighbouring countries, rather than to more geographically distant high-income countries ( [[#Hoffmann--2020|Hoffmann et al., 2020]] ; [[#Kaczan--2020|Kaczan and Orgill-Meyer, 2020]] ). Natural disaster-related displacements in sub-Saharan Africa were over 2.6 million in 2018 and 3.4 million in 2019 (13.9% of the global total and one of the highest historical figures for the region), with east (1,437,7000) and west Africa (798,000) being hotspots in 2018 (Table Box 9.8.1; [[#Mastrorillo--2016|Mastrorillo et al., 2016]] ; [[#IDMC--2019|IDMC, 2019]] ; [[#IDMC--2020|IDMC, 2020]] ). Estimates indicate future climate change effects on internal migration in Africa will be considerable (Table Box 9.8.1; [[#Rigaud--2018|Rigaud et al., 2018]] ).

Internal migration, displacement and urbanisation

Climate change can have opposing influences on migration flows. Deteriorating economic conditions caused by climate hazards can encourage out-migration ( [[#Wiederkehr--2018|Wiederkehr et al., 2018]] ). However, these same economic losses undermine household resources needed to migrate ( [[#Cattaneo--2016|Cattaneo and Peri, 2016]] ). The net effect of these two forces leads to mixed results across study methodologies and contexts ( [[#Carleton--2016|Carleton and Hsiang, 2016]] ; [[#Borderon--2019|Borderon et al., 2019]] ; [[#Cattaneo--2019|Cattaneo et al., 2019]] ; [[#Hoffmann--2020|Hoffmann et al., 2020]] ).

Urbanisation in Africa is affected by climate conditions in rural agricultural areas ( ''high confidence'' ). Urbanisation can increase when reduced moisture availability depresses farm incomes or pastoral livelihoods become unviable ( [[#Marchiori--2012|Marchiori et al., 2012]] ; [[#Henderson--2014|Henderson et al., 2014]] ; [[#Mastrorillo--2016|Mastrorillo et al., 2016]] ). The influence of rainfall on rural–urban migration increased since decolonisation, possibly due to more lenient legislation on internal mobility, with each 1% reduction in precipitation below a long-term average associated with a 0.45% increase in urbanisation ( [[#Barrios--2006|Barrios et al., 2006]] ). The rate of rural–urban migration is anticipated to increase ( [[#Neumann--2015|Neumann et al., 2015]] ) as a result of increasing vulnerability of agricultural livelihoods to climate change ( [[#Serdeczny--2017|Serdeczny et al., 2017]] ). Nevertheless, rural–urban migration is not a simple one-way process. Peri-urban and rural areas provide developmental feedback loops, helping create a ‘regional agglomeration’ effect, for instance, through growing food demand, family and social connections, and flows back to rural areas of goods and services and financial investments ( [[#UN-Habitat--2016|UN-Habitat, 2016]] ; [[#Dodman--2017|Dodman et al., 2017]] ).

Migration is an important and potentially effective climate change adaptation strategy in Africa and must be considered in adaptation planning ( ''high confidence)'' ( [[#Williams--2021|Williams et al., 2021]] ). The more agency migrants have (that is, degree of voluntarity and freedom of movement), the greater the potential benefits for sending and receiving areas ( ''high agreement, medium evidence'' ) (Cross-Chapter Box MIGRATE in Chapter 7). In a synthesis of 63 studies covering over 9700 rural households in dryland sub-Saharan Africa, 23% of households employed migration (primarily temporary economic) to adapt to changes in rainfed agriculture ( [[#Wiederkehr--2018|Wiederkehr et al., 2018]] ). Migration responses to climate change tend to be stronger among wealthier households, as poorer households often lack financial resources necessary to migrate ( [[#Kaczan--2020|Kaczan and Orgill-Meyer, 2020]] ).

International migration

Studies on propensity to emigrate have uncovered conflicting results. Some findings suggest in low-income countries high temperatures ‘trap’ people at home and lower migration rates abroad, but in middle-income countries, these same high temperatures encourage emigration ( [[#Cattaneo--2016|Cattaneo and Peri, 2016]] ). However, other research finds in poor and agriculturally dependent countries, high temperatures encourage international out-migration, particularly to the OECD ( [[#Cai--2016|Cai et al., 2016]] ). Some evidence indicates people who leave tend to be more educated, possibly leading to ‘brain drain’ ( [[#Mbaye--2017|Mbaye, 2017]] ). Recent evidence suggests hotter-than-normal temperatures across 103 countries, including many in Africa, increased asylum applications to the European Union ( [[#Missirian--2017|Missirian and Schlenker, 2017]] ). Assuming no change in present-day vulnerability, asylum applications are projected to increase 34% across Africa (relative to 2000–2014) at 2.2°C global warming ( [[#Missirian--2017|Missirian and Schlenker, 2017]] ), although this finding has been challenged in the literature ( [[#Abel--2019|Abel et al., 2019]] ; [[#Boas--2019|Boas et al., 2019]] ).

International remittances are a vital resource for developing countries that can help aid recovery from climate shocks (Hallegatte et al. 2016). Estimated at USD 48 billion in 2019 their importance is expected to grow further due to foreign direct investment declines during the COVID-19 pandemic ( [[#World%20Bank--2020a|World Bank, 2020a]] ). Furthermore, domestic remittances from rural–urban migration can help rural households respond to climate risks ( [[#KNOMAD--2016|KNOMAD, 2016]] ). However, adequate finance and banking infrastructure are essential for remittances and, on average, cash transfer costs for sub-Saharan African countries remain the highest globally ( [[#World%20Bank--2020a|World Bank, 2020a]] ). Mobile money technologies and regulation that promotes competition in the remittances market can reduce transaction costs ( [[#World%20Bank--2020a|World Bank, 2020a]] ). Governments can further address challenges facing internal and international migrants by including them in health services and other social programmes and protecting them from discrimination ( [[#World%20Bank--2020a|World Bank, 2020a]] ).

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Box 9.8

'''Table Box 9.8.1 |''' Reported impacts of climate on migration in Africa. (Findings on the linkages between climatic conditions and migration vary greatly across countries in Africa.)

{| class="wikitable"
|-
! '''Climate driver'''

! '''Country'''

! '''Climate – Migration linkages'''

! '''Reference'''

|-
| rowspan="3"| ''Temperature''

| Kenya

| Cool temperatures linked to internal labour migration among males.

| [[#Gray--2016|Gray and Wise (2016)]]

|-
| Uganda

| High temperatures linked to increased non-labour migration among females.

Short hot spells linked to increased temporary migration. Long-term heat stress linked to permanent migration through an agricultural livelihoods pathway.

| [[#Gray--2016|Gray and Wise (2016)]] ; [[#Call--2020|Call and Gray (2020)]]

|-
| Tanzania

| Temperature-induced income shocks linked to decreased long-term rural–urban migration among men.

| [[#Hirvonen--2016|Hirvonen (2016)]]

|-
| rowspan="8"| ''Precipitation''

| Kenya

| Increased precipitation linked to decreased rural–urban migration.

| Mueller et al. (2020)

|-
| Zambia

| Increased precipitation linked to increased internal migration.

| Mueller et al. (2020)

|-
| Burkina Faso

| Drier regions linked to increased temporary and permanent migrations to other rural areas. Short-term precipitation deficits linked to increased long-term migration to rural areas and decreased risk of short-term migration to distant destinations.

| Henry et al. (2004)

|-
| Ethiopia

| Drought linked to men’s rural–urban labour migration, especially in land-poor households.

Drought linked to decreased marriage-related migration by women.

Precipitation variability and drought linked to rural–urban labour migration.

Precipitation variability and drought linked to out-migration to communities where precipitation variability and drought probability are lower.

High precipitation variability linked to increased migration, either through increased non-farm activities, which enable migration through economic resources or through insufficient agricultural production, which increase migration needs.

| [[#Gray--2012|Gray and Mueller (2012)]] ; [[#Morrissey--2013|Morrissey (2013)]] ; Hermans-Neumann et al. (2017); [[#Groth--2021|Groth et al. (2021)]]

|-
| Ghana

| Increased severity of drought and household insecurity linked to reduced future migration intentions of households.

| [[#Adger--2021|Adger et al. (2021)]]

|-
| Malawi

| Precipitation shocks linked to rural out-migration to communities where precipitation variability and drought probability are lower.

Precipitation shocks (flood and droughts) linked to longer-term urban migration and/or reverse (i.e., urban–rural) migration.

| Lewin et al. (2012); [[#Suckall--2015|Suckall et al. (2015)]]

|-
| Mali

| Decreased precipitation linked to overall increase in out-migration—where farming families or individuals from farming communities will leave their origin community—and some changes in duration and destination of trips. These moves can be either permanent or short-term, domestic or international.

| [[#Grace--2018|Grace et al. (2018)]]

|-
| Niger

| Drought linked to economically induced migration of households from rural areas to cities. Drought also linked to temporary international migration.

| [[#Afifi--2011|Afifi (2011)]]

|-
| rowspan="7"| ''Temperature and precipitation''

| Burkina Faso

| High temperatures linked to negative effects on all migration streams including international migration, much of which is to neighbouring countries. International migration also declines with precipitation.

| [[#Gray--2016|Gray and Wise (2016)]]

|-
| Senegal

| No detected linkages between climate and migration.

| [[#Gray--2016|Gray and Wise (2016)]]

|-
| Nigeria

| No detected linkages between climate and migration.

| [[#Gray--2016|Gray and Wise (2016)]]

|-
| Botswana

| Increased temperatures and precipitation linked to decreased internal migration.

| Mueller et al. (2020)

|-
| South Africa

| Higher temperatures and precipitation extremes linked to increased rural out-migration, especially among black and low-income South Africans.

| [[#Mastrorillo--2016|Mastrorillo et al. (2016)]]

|-
| Senegal

| Precipitation variability, drought and increased temperatures linked to seasonal migration from rural to urban areas.

| [[#Hummel--2016|Hummel (2016)]]

|-
| Zambia

| Hotter and drier climate linked to inter-district migration of wealthy districts. Poor districts characterised by climate-related immobility.

| [[#Nawrotzki--2018|Nawrotzki and DeWaard (2018)]]

|}

'''Table Box 9.8.2 |''' Projected numbers and shares of internal climate migrants in 2050 by sub-regions of sub-Saharan Africa. Projections are for internal migration driven by three slow-onset climate hazards (water stress, crop failure and SLR), and excluding rapid-onset hazards such as floods and tropical cyclones. As such, they present a lower-bound estimate of potential climate change impacts on internal migration. Projections are for two warming scenarios: low emissions (RCP2.6) and high emissions (RCP8.5), both coupled with a socioeconomic pathway (SSP4) in which low-income countries have high population growth, high rates of urbanisation, and increasing inequality within and among countries. By 2050, between 17.4 million (RCP2.6) and 85 million (RCP8.5) people (up to 4% of the region’s total population) could be moving as a consequence of climate impacts on water stress, crop productivity and SLR. More inclusive socioeconomic pathways with lower population growth are projected to reduce these risks. West Africa has the highest levels of climate migrants, potentially reaching more than 50 million, suggesting that climate impacts will have a particularly pronounced impact on future migration in the region. In east Africa, out-migration hotspots include coastal regions of Kenya and Tanzania, western Uganda and parts of the northern highlands of Ethiopia. Kampala, Nairobi and Lilongwe may become hotspots of climate in-migration, coupled with existing rural to urban migration trends, and a high likelihood of movement toward non-climate-related sources of income in cities. Source: ( [[#Rigaud--2018|Rigaud et al., 2018]] ).

{| class="wikitable"
|-
! '''Region'''

!
! colspan="2"| '''Global warming around 2.5°C above pre-industrial by 2050 (RCP8.5)'''

! colspan="2"| '''Global warming around 1.7°C above pre-industrial by 2050 (RCP2.6)'''

|-
| rowspan="2"| ''East Africa''

| Average number of internal migrants by 2050 (million)

| colspan="2"| 10.1

| colspan="2"| 6.9

|-
| Internal climate migrants as percent of population

| colspan="2"| 1.28%

| colspan="2"| 0.87%

|-
| rowspan="2"| ''West Africa''

| Average number of internal migrants by 2050 (million)

| colspan="2"| 54.4

| colspan="2"| 17.9

|-
| Internal climate migrants as percent of population

| colspan="2"| 6.87%

| colspan="2"| 2.27%

|-
| rowspan="2"| ''Central Africa''

| Average number of internal migrants by 2050 (million)

| colspan="2"| 5.1

| colspan="2"| 2.6

|-
| Internal climate migrants as percent of population

| colspan="2"| 1.31%

| colspan="2"| 0.66%

|-
| rowspan="2"| ''Southern Africa''

| Average number of internal migrants by 2050 (million)

| colspan="2"| 1.5

| colspan="2"| 0.9

|-
| Internal climate migrants as percent of population

| colspan="2"| 2.31%

| colspan="2"| 1.40%

|-
| rowspan="4"| ''Sub-Saharan Africa''

| Average number of internal migrants by 2050 (million)

| colspan="2"| 71.1

| colspan="2"| 28.3

|-
| Minimum (left) and maximum (right) million

| 56.6

| 85.7

| 17.4

| 39.9

|-
| Internal climate migrants as percent of population

| colspan="2"| 3.49%

| colspan="2"| 1.39%

|-
| Minimum (left) and maximum (right) percent

| 2.71%

| 4.03%

| 0.91%

| 2.04%

|}

Box 9.8

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