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

== 9.9 Human Settlements and Infrastructure ==

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This section assesses climate impacts, risks and adaptation options for human settlements comprising human populations and infrastructure such as buildings, roads and energy across Africa.

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=== 9.9.1 Urbanisation, Population and Development Trends ===

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Africa is the most rapidly urbanising region in the world, with an annual urban population growth rate of 3.6% for 2005–2015 ( [[#UN-Habitat--2016|UN-Habitat, 2016]] ). About 57% of the population currently lives in rural areas, but the proportion of the population living in urban areas is projected to exceed 60% by 2050 ( [[#UNDESA--2019b|UNDESA, 2019b]] ) ( [[#UN-Habitat--2016|UN-Habitat, 2016]] ). Much of the rapid rate of urbanisation has resulted from the growth of small towns and intermediary cities (African Development Bank et al., 2016).

Approximately 59% of sub-Saharan Africa’s urban population resides in informal settlements (in some cities up to 80%), and the population in informal settlements is expected to increase ( ''very high confidence'' ) ( [[#Taylor--2014|Taylor and Peter, 2014]] ; [[#UN-Habitat--2014|UN-Habitat, 2014]] ; 2016; UNDP, 2019). These urbanisation trends are compounding increasing exposure to climate hazards, particularly floods and heatwaves ( ''high confidence'' ) ( [[#Dodman--2015|Dodman et al., 2015]] ).

Globally, the highest rates of population growth and urbanisation are taking place in Africa’s coastal zones ( ''high confidence'' ) ( [[#Merkens--2016|Merkens et al., 2016]] ). Coastal urban populations account for 25–29% of the total population in west, north and southern Africa ( [[#OECD/SWAC--2020|OECD/SWAC, 2020]] ). Accounting for a continuing young population, stagnant economies and migration to regional growth centres, projections indicate that the low-lying coastal zone population of Africa could increase to over 100 million people by 2030 and over 200 million people by 2060 relative to 54 million in 2000 ( [[#Neumann--2015|Neumann et al., 2015]] ; see Figure 9.28).

Climate-related displacement is widespread in Africa, with increased migration to urban areas in sub-Saharan Africa linked to decreased rainfall in rural areas, increasing urbanisation and affecting household vulnerability (see Box 9.9). Much of this growth can occur in informal settlements which are growing due to both climatic and non-climatic drivers, and which often house temporary migrants, including internally displaced people. Such informal settlements are located in areas exposed to climate change and variability and are exposed to floods, landslides, sea level rise and storm surges in low-lying coastal areas, or alongside rivers that frequently overflow, thereby exacerbating existing vulnerabilities ( [[#Satterthwaite--2020|Satterthwaite et al., 2020]] ).

Sub-Saharan Africa’s large infrastructure deficit (quantity, quality and access) with respect to road transport, electricity, water supply and sanitation places the region at the lowest of all developing regions ( [[#AfDB--2018a|AfDB, 2018a]] ; [[#Calderon--2018|Calderon et al., 2018]] ). Adequate infrastructure to support Africa’s rapidly growing population is important to raise living standards and productivity in informal settlements ( [[#AfDB--2018b|AfDB, 2018b]] ; [[#UN%20Environment--2019|UN Environment, 2019]] ). Yet planned infrastructure developments, including those related to the AU’s PIDA, along with other energy plans, and China’s Belt and Road Initiative, may increase or decrease both climate change mitigation and adaptation depending on whether infrastructure planning integrates current and future climate change risks ( [[#Cervigni--2015|Cervigni et al., 2015]] ; [[#Addaney--2020|Addaney, 2020]] ; see Box 9.5).

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<span id="observed-impacts-on-human-settlements-and-infrastructure"></span>
=== 9.9.2 Observed Impacts on Human Settlements and Infrastructure ===

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African human settlements are particularly exposed to floods (pluvial and fluvial), droughts and heat waves. Other climate hazards are sea level rise and storm surges in coastal areas, tropical cyclones and convective storms. This sub-section provides an assessment of observed impacts and risks from climate hazards in different sub-regions to underscore the relevance of climate-sensitive planning and actions to advance social and economic development, and reduce the loss and damage of property, assets and critical infrastructure.

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<span id="observed-impacts-on-human-settlements"></span>
==== 9.9.2.1 Observed Impacts on Human Settlements ====

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The spatial distribution of climate hazards and observed impacts in terms of total people affected (displaced persons and deaths) during 2010–2020 is shown in Figure 9.27. From 2000–2019, floods and droughts accounted for 80% and 16%, respectively, of the 337 million affected persons, and a further 32% and 46%, respectively, of 46,078 deaths from natural disasters in Africa ( [[#CRED--2019|CRED, 2019]] ). Flooding is a major hazard across Africa ( [[#Kundzewicz--2014|Kundzewicz et al., 2014]] ; [[#Douglas--2017|Douglas, 2017]] ) and is increasing ( [[#Zevenbergen--2016|Zevenbergen et al., 2016]] ; [[#Elboshy--2019|Elboshy et al., 2019]] ). An increase in extreme poverty and up to a 35% decrease in consumption has been associated with exposure to flood shocks ( [[#Azzarri--2020|Azzarri and Signorelli, 2020]] ). Sub-Saharan Africa is the only region globally that did not show decreasing rates of flood mortality since the 1990s ( [[#Tanoue--2016|Tanoue et al., 2016]] ). Economic opportunities, transportation of goods and services, and mobility and access to essential services, including health and education, are greatly hindered by flooding ( [[#Gannon--2018|Gannon et al., 2018]] ). Severe impacts from tropical cyclone landfalls have been recorded in east and southeastern Africa ( [[#Rapolaki--2018|Rapolaki and Reason, 2018]] ; [[#Cambaza--2019|Cambaza et al., 2019]] ; [[#Chatiza--2019|Chatiza, 2019]] ; [[#Hope--2019|Hope, 2019]] ). Cyclones Idai and Kenneth in early 2019 caused flooding of districts in Malawi, Mozambique and Zimbabwe, with substantial loss and damage to infrastructure in the energy, transport, water supply, communication services, housing, health and education sectors, particularly in Mozambique (Figure 9.27; see also Cross-Chapter Box DISASTER in Chapter 4; [[#Warren--2019|Warren, 2019]] ; [[#Dube--2021|Dube et al., 2021]] ; [[#Phiri--2021|Phiri et al., 2021]] ).

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[[File:da8409e06186a268e4a12de3eaa98b66 IPCC_AR6_WGII_Figure_9_027.png]]

'''Figure 9.27 |''' '''From 2010–2020, over 166 million people were reported to be affected by climate hazards across Africa.''' Maps show

'''(a)''' location of all reported climate hazards;

'''(b)''' people affected by droughts;

'''(c)''' people affected by convective storms;

'''(d)''' people affected by floods,

'''(e)''' total deaths from tropical cyclones, and

'''(f)''' total deaths from heat waves. Source: [[#EMDAT%20and%20CRED--2020|EMDAT and CRED (2020)]] . Note: Although extreme weather damage databases under-report heatwaves (which is indicated in panel (f) by very few deaths), the region has experienced a number of heatwaves and will be affected disproportionately by them in the future under climate change ( [[#Harrington--2020|Harrington and Otto, 2020]] ).

'''Table 9.7 |''' Case studies of climate hazard impacts and risks to selected human settlements in Africa

{| class="wikitable"
|-
! Hazard

! Country/City

! Impact on Human Settlement and Infrastructure

! Source

|-
| Sea level rise and storm surge

| Egypt

(North Africa)

| '''December 2010, January 2011 and October 2015:''' Storm surge of 1.2 m.a.s. l. (metres above sea level) (typical of the Nile Delta coast: 0.4–0.5 m). Coastal flooding and damage to some coastal structures. Moderate flooding of the Nile Delta lowlands.

Alexandria city: Flooding generated by heavy rainfall (2015). Increased turbidity of water sources affected efficiency of water treatment plants leading to reduction of water supplies affecting public health systems. Potable water supply affected by saltwater intrusion. Coastal erosion and property damage.

| [[#Kloos--2015|Kloos and Baumert (2015)]] ; Abutaleb et al. (2018)

Eldeberky Y (2015); [[#Yehia--2017|Yehia et al. (2017)]]

|-
| rowspan="2"| Drought

| Southern Africa

| '''El Niño drought, 2015–2016:''' Western Cape Region affected 8.6 million people. Losses: &gt;USD 2.2 billion. Power generation reduced by 75% at Kariba dam (Zambia) in 2016, and the Cahora Bassa dam (Mozambique) reduced to 34% of its capacity with widespread impact on electricity supplies across southern Africa.

| Davis-Reddy et al. (2017); Spalding-Fecher et al. (2017)

[[#Brooks--2019|Brooks (2019)]]

|-
| Somalia

(East Africa)

| '''Somalia drought, 2016–2017:''' 926,000 newly displaced people reported (November 2016–October 2017). Around 40% of total drought-related displacements accommodated in Mogadishu, Baidoa, Kismayo; 60% hosted in other secondary cities. Increased population density and overcrowding in Somalia’s urban areas. Explosion of new shelters and tents for displaced persons within and in outskirts of cities. In Mogadishu, 34% of new settlements developed within 6 months.

| [[#Government%20of%20Somalia--2018|Government of Somalia (2018)]]

|-
| rowspan="2"| Flooding

| Malawi

(East Africa)

| '''Floods, 2019:''' Approximately 975,600 people affected, 672 injured, 60 persons killed and 86,976 people displaced. 288,371 houses damaged. 129 bridges and 68 culverts destroyed. Around 1841 km of road network estimated at USD 36.1 million destroyed. Total cost of damage and losses: housing sector, USD 106.9 million; energy, USD 3.1 million; water and sanitation, USD 6.4 million; transport, USD 37.0 million. Total cost of destroyed physical assets, USD 157.7 million. Damage and losses in Blantyre city: housing sector, USD 29.87 million; energy sector, USD 0.38 million; transport sector, USD 1.72 million.

| [[#Government%20of%20Malawi--2019|Government of Malawi (2019)]]

|-
|
|-
| Tropical cyclone

| Mozambique, Zimbabwe and Malawi (southern Africa)

| '''Cyclones Idai and Kenneth, 2019:''' Severe flooding of districts in Mozambique, Zimbabwe, and Malawi; 233,900 houses completely destroyed or damaged in Mozambique.

Cyclone Kenneth: about 40,000 houses and 19 health facilities destroyed.

Cyclone Idai: destroyed or damaged 1345 km of transmission lines, 10,216 km of distribution lines, two 90 MW generation plants, 30 sub-stations and 4000 transformers, resulting in estimated damage of USD 133.5 million and loss of USD 47.9 million in the energy sector in Mozambique. 602 and 299 people killed in Mozambique and Zimbabwe, respectively; about 1.5 million people affected in Mozambique and 270,000 in Zimbabwe.

In Beira (Mozambique), 60% of city was inundated, 70% of houses damaged or totally destroyed, mostly in the poorest neighbourhood, and 90% of the city’s power grid affected. Huge losses and damages to infrastructures in the energy, transport, water supply, communication services, housing, health and education sectors were also recorded.

| ( [[#Cambaza--2019|Cambaza et al., 2019]] ; [[#Chatiza--2019|Chatiza, 2019]] ; [[#Government%20of%20Mozambique--2019|Government of Mozambique, 2019]] ; [[#Hope--2019|Hope, 2019]] ; [[#Lequechane--2020|Lequechane et al., 2020]] ; [[#Phiri--2021|Phiri et al., 2021]] )

( [[#Enenkel--2020|Enenkel et al., 2020]] )

|-
| rowspan="2"| Landslide

| Freetown

(West Africa)

| '''August, 2017:''' At least 500 people killed and over 600 people declared missing, &gt;3000 residents rendered homeless; 349 houses destroyed. Damage to health facilities and educational buildings. Economic cost of landslide and flood, USD 31.6 million.

| ( [[#Cui--2019|Cui et al., 2019]] )

( [[#World%20Bank--2017b|World Bank, 2017b]] )

|-
| Uganda

(East Africa)

| '''Slopes of Mt Elgon, 2010:''' More than 350 deaths and 500,000 people needed to be relocated.

| ( [[#Croitoru--2019|Croitoru et al., 2019]] )

|}

From 2005–2020, flood-induced damage over Africa was estimated at over USD 4.4 billion, with eastern and western Africa being the most affected regions ( [[#EMDAT%20and%20CRED--2020|EMDAT and CRED, 2020]] ). Total damages in four west African countries (Benin, Cote d’Ivoire, Senegal and Togo) in 2017 were estimated at USD 850 million for pluvial floods and USD 555 million for fluvial floods ( [[#Croitoru--2019|Croitoru et al., 2019]] ). Unprecedented economic loss, in terms of goods and properties, estimated by the Nigerian insurance industry at USD 200 million resulted from floods in Lagos in 2011 ( [[#Adelekan--2016|Adelekan, 2016]] ). In southern Africa, the highest costs were incurred from flood losses during the period 2000–2015 ( [[#UNEP-FI--2019b|UNEP-FI, 2019b]] ; [[#Simpson--2020|Simpson, 2020]] ).

Business disruptions from climate impacts have implications for deepening poverty ( [[#Adelekan--2015|Adelekan and Fregene, 2015]] ). Small and medium enterprises (SMEs) employ 60–90% of workers in many African countries and contribute 40% or more to the GDP in Ghana, Kenya, Nigeria, South Africa, Tanzania and Zimbabwe ( [[#Muriithi--2017|Muriithi, 2017]] ). The viability of businesses and economic well-being of large populations employed in SMEs is severely affected by climate hazards as reported for local wind storms in Ibadan ( [[#Adelekan--2012|Adelekan, 2012]] ), El Niño-related flooding (Nairobi), drought-induced water supply disruption (Gaborone) and power outages (Lusaka) ( [[#Gannon--2018|Gannon et al., 2018]] ). High water demand due to high rates of urbanisation and population growth, coupled with drought, reduce groundwater levels in cities (e.g., Bouake, Harare, Tripoli, Niamey) and increase saltwater intrusion into groundwater in coastal areas, reducing water availability and water security, particularly for poorer populations not connected to municipal water networks ( [[#Aswad--2019|Aswad et al., 2019]] ; [[#Claon--2020|Claon et al., 2020]] ).

Evidence of the impact of heat waves in urban Africa in the current climate is sparse, due in part to low reporting and monitoring ( [[#Engelbrecht--2015|Engelbrecht et al., 2015]] ; [[#Harrington--2020|Harrington and Otto, 2020]] ). Knowledge is also limited on the interaction of climate change, urban growth and the urban heat island effect in Africa ( [[#Chapman--2017|Chapman et al., 2017]] ). In north Africa, the present-day number of high heat stress nights is around 10 times larger in urban than rural areas ( [[#Fischer--2012|Fischer et al., 2012]] ).

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<span id="observed-impacts-to-road-and-energy-infrastructure"></span>
==== 9.9.2.2 Observed Impacts to Road and Energy Infrastructure ====

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The highest transport infrastructure exposures are from floods ( [[#Koks--2019|Koks et al., 2019]] ), with potentially severe consequences for food security ( [[#Fanzo--2018|Fanzo et al., 2018]] ), communication and the economy of affected regions ( ''high confidence'' ) ( [[#Koks--2019|Koks et al., 2019]] ). Eight of the 20 countries with the highest expected annual damages to road and rail assets, relative to the country’s GDP, are located in east, west and central Africa ( [[#Koks--2019|Koks et al., 2019]] ). Transport impacts compound climate impacts, such as heat stress and air pollution linked to vehicle emissions in Dar es Salaam ( [[#Ndetto--2014|Ndetto and Matzarakis, 2014]] ).

African economies that rely primarily on hydropower for electricity generation are particularly sensitive to climate variability ( [[#Brooks--2019|Brooks, 2019]] ). This sensitivity was already felt during the 2015/16 El Niño, in which Malawi, Tanzania, Zambia and Zimbabwe all experienced widespread and prolonged load shedding due to low rainfall. The impact was felt throughout the economy and reflected in reduced GDP growth in Zambia ( [[#Conway--2017|Conway et al., 2017]] ).

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=== 9.9.3 Observed Vulnerabilities of Human Settlements to Climate Risks ===

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Urban vulnerabilities and exposure to climate change are increasing ( ''medium to high confidence'' ) and are influenced by patterns of urban settlement and housing characteristics ( [[#Satterthwaite--2017|Satterthwaite, 2017]] ; [[#Godsmark--2019|Godsmark et al., 2019]] ; [[#Williams--2019a|Williams et al., 2019a]] ). About 70% of African cities are highly vulnerable to climate shocks of which small- and medium-sized towns and cities are more at risk (Verisk Maplecroft, 2018). Flooding was perceived as the most prominent water risk in 75% of 36 sampled cities across African sub-regions, while drought-related water scarcity was indicated as very important/important in 66.7% of cities ( [[#OECD--2021|OECD, 2021]] ). Almost one-third of African cities with populations of 300,000 or more are located in areas of high exposure to at least one natural hazard, including floods (11%) and droughts (20–25%) using natural hazard data for the period 1970s to early 2000s ( [[#Gu--2015|Gu et al., 2015]] ). The coastal cities of east, west and north Africa are particularly vulnerable to the effects of rising sea levels ( [[#Abutaleb--2018|Abutaleb et al., 2018]] ; [[#IPCC--2019a|IPCC, 2019a]] ). From 2000–2015, the proportion of people exposed to floods increased for most African countries, with Mozambique and multiple countries in West Africa estimated to have had the proportion of their populations exposed to flooding increase more than 50% ( [[#Tellman--2021|Tellman et al., 2021]] ).

Globally, sub-Saharan Africa has the largest population living in extreme poverty that are exposed to high flood risk (~71 million people or 55% of global total) ( [[#Rentschler--2020|Rentschler and Salhab, 2020]] ). Poverty is a significant factor of flood-induced displacement in Africa, where even small flood exposure can lead to high numbers being displaced ( [[#Kakinuma--2020|Kakinuma et al., 2020]] ). Africa’s large population of urban poor and marginalised groups and informal sector workers, further contribute to high vulnerability to extreme weather and climate change in many settlements ( ''high confidence'' ) ( [[#Adelekan--2015|Adelekan and Fregene, 2015]] ; [[#IPCC--2019a|IPCC, 2019a]] ; UNDP, 2019).

Other non-climatic stressors which exacerbate vulnerabilities, especially in urban areas, include poor socioeconomic development, weak municipal governance, poor resource and institutional capacities, together with multi-dimensional, location-specific inequalities ( ''high confidence'' ) ( [[#Dodman--2017|Dodman et al., 2017]] ; [[#Satterthwaite--2017|Satterthwaite, 2017]] ).

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<span id="projected-risks-for-human-settlements-and-infrastructure"></span>
=== 9.9.4 Projected Risks for Human Settlements and Infrastructure ===

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<span id="projected-risks-for-human-settlements"></span>
==== 9.9.4.1 Projected Risks for Human Settlements ====

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The extent of urban areas in Africa exposed to climate hazards will increase considerably and cities will be hotspots of climate risks, which could amplify pre-existing stresses related to poverty, exclusion and governance ( ''high confidence'' ) ( [[#IPCC--2018b|IPCC, 2018b]] ).

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===== 9.9.4.1.1 Flooding =====

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Continuing current population and GDP growth trends, the extent of urban land exposed to high-frequency flooding is projected to increase around 270% in north Africa, 800% in southern Africa, and 2600% in mid-latitude Africa by 2030 when compared to 2000, without considering climate change ( [[#Güneralp--2015|Güneralp et al., 2015]] ). In addition, global warming is projected to increase frequency and magnitude of river floods in east, central and west Africa ( [[#Alfieri--2017|Alfieri et al., 2017]] ; [[#Gu--2020|Gu et al., 2020]] ; [[#Kam--2021|Kam et al., 2021]] ). On average, across large African river basins, the frequency of flood events with a current return period of 100 years is projected to increase to 1 in 40 years at 1.5°C and 2°C global warming, and 1 in 21 years at 4°C warming, with Egypt, Nigeria, Sudan and the Democratic Republic of Congo in the top 20 countries globally for projected damages ( [[#Alfieri--2017|Alfieri et al., 2017]] ). Compared to population in 2000, human displacement due to river flooding in sub-Saharan Africa is projected to increase 600% by 2066–2096 with moderate-to-high population growth and 2.6°C global warming, with risk reducing to a 200% increase for low population growth and 1.6°C global warming ( [[#Kam--2021|Kam et al., 2021]] ).

Urban population exposure to tropical cyclone hazards in southeastern Africa, in particular Mozambique, is projected to increase due to the intensification of cyclones and their extended duration associated with warmer sea surface temperatures ( [[#Fitchett--2018|Fitchett, 2018]] ; [[#Vidya--2020|Vidya et al., 2020]] ). Urban damage assessment based on a 10-year flood protection level for Accra, Ghana, shows that without flood protection, there is a 10% probability of a flood occurring annually which could cause USD 98.5 million urban damage, affect GDP by USD 50.3 million and affect 34,000 people ( [[#Asumadu-Sarkodie--2015|Asumadu-Sarkodie et al., 2015]] ). Many urban households and Africa’s growing assets could therefore be exposed to increased flooding ( [[#IPCC--2018b|IPCC, 2018b]] ).

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<span id="sea-level-rise-and-coastal-flooding"></span>
===== 9.9.4.1.2 Sea level rise and coastal flooding =====

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Africa’s low-lying coastal zone population is expected to grow more than any other region from 2000 to 2060 (see Figure 9.28; [[#Neumann--2015|Neumann et al., 2015]] ). Future rapid coastal development is expected to increase existing high vulnerabilities to sea level rise (SLR) and coastal hazards, particularly in east Africa ( ''high confidence'' ) (Figure 9.29; [[#Hinkel--2012|Hinkel et al., 2012]] ; [[#Kulp--2019|Kulp and Strauss, 2019]] ). By 2100, sea levels are projected to rise at least 40 cm above those in 2000 in a below 2°C scenario, and possibly up to 1 m by the end of the century under a 4°C warming scenario ( [[#Serdeczny--2017|Serdeczny et al., 2017]] ; see also Cross-Chapter Box SLR in Chapter 3).

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[[File:112cbee81c32c023e3fdb2f8c1e6b9b0 IPCC_AR6_WGII_Figure_9_028.png]]

'''Figure 9.28 |''' '''Tens to hundreds of millions of people in Africa are projected to be exposed to sea level rise, with a major risk driver being increased exposure due to population increase in low-lying areas.'''

'''(a)''' Population in the low-elevation coastal zone (LECZ) projected to be exposed to mean sea level rise (SLR) for 2030 (+10 cm SLR) and 2060 (+21 cm SLR). Scenarios A, C have exclusive social, political and economic governance whereas scenarios B and D have inclusive social, political and economic governance.

'''(b)''' African countries with the highest projected population numbers in the LECZ, and also the additional population projected to be exposed in these countries due to a 1-in-100 year storm surge event. For panel b projections of population exposure used the high population growth socio-economic scenario (scenario C). Data sourced from [[#Neumann--2015|Neumann et al. (2015)]] .

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[[File:5a391848b4f227f3ceb881fd706a9c8d IPCC_AR6_WGII_Figure_9_029.png]]

'''Figure 9.29 |''' '''Multiple large African cities will be exposed to sea level rise (SLR), these include the selected examples:''' '''(a)''' Dar es Salaam, Bagamoyo, and Stone Town in Tanzania (east Africa), '''(b)''' Lagos in Nigeria, and Cotonou and Porto-Novo in Benin (west Africa) and '''(c)''' Cairo and Alexandria in Egypt (north Africa). Orange shows built-up area in 2014. Shades of blue show permanent flooding due to SLR by 2050 and 2100 under low (RCP2.6), intermediate (RCP4.5) and high (RCP8.5) greenhouse gas emissions scenarios. Darker colours for higher emissions scenarios show areas projected to be flooded in addition to those for lower emissions scenarios. The figure assumes failure of coastal defences in 2050 and 2100. Some areas are already below current SLR and coastal defences need to be upgraded as SLRs (e.g., in Egypt), others are just above mean sea levels and they do not necessarily have high protection levels, so these defences need to be built (e.g., Dar es Salaam and Lagos). Blue shading shows permanent inundation surfaces predicted by Coastal Digital Elevation Model (DEM) and Shuttle Radar Topography Mission (SRTM) given the 95th percentile K14/RCP2.6, RCP4.5 and RCP8.5, for present day, 2050, and 2100 sea level projection for permanent inundation (inundation without a storm surge event), and RL10 (10-year return level storm) ( [[#Kulp--2019|Kulp and Strauss, 2019]] ). Low-lying areas isolated from the ocean are removed from the inundation surface using connected components analysis. Current water bodies are derived from the SRTM Water Body Dataset. Orange areas represent the extent of coastal human settlements in 2014 ( [[#Corbane--2018|Corbane et al., 2018]] ). See Figure CCP4.7 for projections including subsidence and worst-case scenario projections for 2100.

In the absence of any adaptation, Egypt, Mozambique, and Nigeria are projected to be worst affected by SLR in terms of the number of people at risk of flooding annually in a 4°C warming scenario ( [[#Hinkel--2012|Hinkel et al., 2012]] ). Recent estimates have explored the potential damages due to SLR and coastal extreme events in 12 major African cities using a stochastic approach to account for uncertainty ( [[#Abadie--2020|Abadie et al., 2020]] ). The aggreate of expected average damages to these cities in 2050 is USD 65 billion for RCP4.5 and USD 86.5 billion for RCP8.5, and USD 137.5 billion under a high-end scenario that incorporates expert opinion on additional ice sheet melting with damages up to (Table 9.8). When considering low-probability, high-damage events, aggregate damage risks can be more than twice as high, reaching USD 187 billion and USD 206 billion under RCP4.5 and RCP8.5 scenarios, respectively, and USD 397 billion under the high-end scenario. City characteristics and exposure play a larger role in expected damages and risk than changes in sea level. The city of Alexandria in north Africa leads the ranking, with aggregate expected damage of USD 36 billion and USD 50 billion under RCP4.5 and RCP8.5 scenarios, respectively, and USD 79.4 billion under a high-end scenario.

Sea level rise and associated episodic flooding are identified as key drivers of projected net migration of 750,000 people out of the east African coastal zone between 2020 and 2050 ( [[#IPCC--2019a|IPCC, 2019a]] ). These trends, alongside the emergence of ‘hotspots’ of climate in- and out-migration (Box 9.8), will have major implications for climate-sensitive sectors and the adequacy of human settlements, including urban infrastructure and social support systems. Actions which could help reduce the number of people being forced to move in distress, include adoption of inclusive and CRD policies, together with targeted investments to manage the reality of climate migration; and mainstreaming climate migration in development planning (Box 9.8).

'''Table 9.8 |''' Regional relative sea level rise (SLR) for 2050 and 2100, and associated aggregated expected damage risks over the period 2020 to 2050 in 12 major African coastal cities under four SLR scenarios. '''(a)''' Regional relative SLR by 2050 and 2100. For SLR, median and 95th percentiles are presented, in centimetres. '''(b)''' Probabilistic damage estimations by 2050 include expected average damages (EAD), damages at the 95th percentile (value at risk; VaR) and the expected shortfall (ES), which represents the average damages of the 5% worst cases. Four relative sea level projections were considered under no adaptation: the RCP2.6, 4.5 and 8.5 scenarios from the ( [[#IPCC--2014a|IPCC, 2014a]] ), and a high-end RCP8.5 scenario that incorporates expert opinion on additional ice sheet melting. Note that figures are provided in undiscounted millions of US dollars (2005) and have been rounded off to avoid a false sense of precision ( [[#Abadie--2020|Abadie et al., 2020]] ; [[#Abadie--2021|Abadie et al., 2021]] ).

{| class="wikitable"
|-
! colspan="13"| (a) Regional relative sea level rise (cm)

|-
! rowspan="2"| City

! rowspan="2"| Year

! colspan="3"| RCP2.6

! colspan="3"| RCP4.5

! colspan="3"| RCP8.5

! colspan="2"| High-end

|-
! colspan="2"| Median

! P95

! colspan="2"| Median

! P95

! colspan="2"| Median

! P95

! Median

! P95

|-
| rowspan="2"| '''Abidjan'''

| '''2050'''

| colspan="2"| '''21'''

| '''30'''

| colspan="2"| '''22'''

| '''32'''

| colspan="2"| '''24'''

| '''34'''

| '''28'''

| '''48'''

|-
| '''2100'''

| colspan="2"| '''44'''

| '''69'''

| colspan="2"| '''53'''

| '''86'''

| colspan="2"| '''75'''

| '''114'''

| '''86'''

| '''206'''

|-
| rowspan="2"| '''Alexandria'''

| '''2050'''

| colspan="2"| '''18'''

| '''26'''

| colspan="2"| '''18'''

| '''28'''

| colspan="2"| '''21'''

| '''30'''

| '''25'''

| '''43'''

|-
| '''2100'''

| colspan="2"| '''36'''

| '''58'''

| colspan="2"| '''46'''

| '''73'''

| colspan="2"| '''67'''

| '''102'''

| '''78'''

| '''186'''

|-
| rowspan="2"| '''Algiers'''

| '''2050'''

| colspan="2"| '''19'''

| '''27'''

| colspan="2"| '''19'''

| '''29'''

| colspan="2"| '''22'''

| '''31'''

| '''25'''

| '''45'''

|-
| '''2100'''

| colspan="2"| '''39'''

| '''62'''

| colspan="2"| '''47'''

| '''76'''

| colspan="2"| '''66'''

| '''98'''

| '''78'''

| '''192'''

|-
| rowspan="2"| '''Cape Town'''

| '''2050'''

| colspan="2"| '''20'''

| '''30'''

| colspan="2"| '''21'''

| '''31'''

| colspan="2"| '''23'''

| '''33'''

| '''27'''

| '''48'''

|-
| '''2100'''

| colspan="2"| '''44'''

| '''69'''

| colspan="2"| '''53'''

| '''87'''

| colspan="2"| '''75'''

| '''117'''

| '''86'''

| '''199'''

|-
| rowspan="2"| '''Casablanca'''

| '''2050'''

| colspan="2"| '''19'''

| '''27'''

| colspan="2"| '''20'''

| '''29'''

| colspan="2"| '''22'''

| '''31'''

| '''26'''

| '''46'''

|-
| '''2100'''

| colspan="2"| '''39'''

| '''63'''

| colspan="2"| '''47'''

| '''78'''

| colspan="2"| '''65'''

| '''99'''

| '''77'''

| '''198'''

|-
| rowspan="2"| '''Dakar'''

| '''2050'''

| colspan="2"| '''21'''

| '''31'''

| colspan="2"| '''21'''

| '''31'''

| colspan="2"| '''23'''

| '''33'''

| '''27'''

| '''48'''

|-
| '''2100'''

| colspan="2"| '''43'''

| '''69'''

| colspan="2"| '''53'''

| '''86'''

| colspan="2"| '''73'''

| '''111'''

| '''85'''

| '''209'''

|-
| rowspan="2"| '''Dar es Salaam'''

| '''2050'''

| colspan="2"| '''20'''

| '''29'''

| colspan="2"| '''21'''

| '''31'''

| colspan="2"| '''24'''

| '''33'''

| '''27'''

| '''47'''

|-
| '''2100'''

| colspan="2"| '''45'''

| '''70'''

| colspan="2"| '''54'''

| '''86'''

| colspan="2"| '''76'''

| '''117'''

| '''87'''

| '''206'''

|-
| rowspan="2"| '''Durban'''

| '''2050'''

| colspan="2"| '''20'''

| '''30'''

| colspan="2"| '''22'''

| '''32'''

| colspan="2"| '''25'''

| '''34'''

| '''28'''

| '''49'''

|-
| '''2100'''

| colspan="2"| '''46'''

| '''72'''

| colspan="2"| '''55'''

| '''90'''

| colspan="2"| '''78'''

| '''119'''

| '''89'''

| '''207'''

|-
| rowspan="2"| '''Lagos'''

| '''2050'''

| colspan="2"| '''21'''

| '''30'''

| colspan="2"| '''22'''

| '''32'''

| colspan="2"| '''24'''

| '''34'''

| '''28'''

| '''48'''

|-
| '''2100'''

| colspan="2"| '''44'''

| '''69'''

| colspan="2"| '''54'''

| '''86'''

| colspan="2"| '''75'''

| '''113'''

| '''86'''

| '''205'''

|-
| rowspan="2"| '''Lome'''

| '''2050'''

| colspan="2"| '''21'''

| '''30'''

| colspan="2"| '''22'''

| '''32'''

| colspan="2"| '''24'''

| '''34'''

| '''28'''

| '''48'''

|-
| '''2100'''

| colspan="2"| '''44'''

| '''69'''

| colspan="2"| '''53'''

| '''86'''

| colspan="2"| '''76'''

| '''115'''

| '''87'''

| '''205'''

|-
| rowspan="2"| '''Luanda'''

| '''2050'''

| colspan="2"| '''21'''

| '''30'''

| colspan="2"| '''23'''

| '''32'''

| colspan="2"| '''25'''

| '''35'''

| '''29'''

| '''49'''

|-
| '''2100'''

| colspan="2"| '''45'''

| '''70'''

| colspan="2"| '''55'''

| '''88'''

| colspan="2"| '''78'''

| '''119'''

| '''90'''

| '''205'''

|-
| rowspan="2"| '''Maputo'''

| '''2050'''

| colspan="2"| '''21'''

| '''31'''

| colspan="2"| '''22'''

| '''32'''

| colspan="2"| '''24'''

| '''34'''

| '''28'''

| '''49'''

|-
| '''2100'''

| colspan="2"| '''45'''

| '''71'''

| colspan="2"| '''55'''

| '''89'''

| colspan="2"| '''78'''

| '''120'''

| '''89'''

| '''209'''

|-
| colspan="13"| '''(b) Expected average damages and risk measures (USD millions)'''

|-
| rowspan="2"| '''City'''

| colspan="3"| '''RCP2.6'''

| colspan="3"| '''RCP4.5'''

| colspan="3"| '''RCP8.5'''

| colspan="3"| '''High-end scenario'''

|-
| '''EAD'''

| '''VaR(95%)'''

| '''ES(95%)'''

| '''EAD'''

| '''VaR(95%)'''

| '''ES(95%)'''

| '''EAD'''

| '''VaR(95%)'''

| '''ES(95%)'''

| '''EAD'''

| '''VaR(95%)'''

| '''ES(95%)'''

|-
| '''Abidjan'''

| '''14,290'''

| '''33,910'''

| '''41,690'''

| '''16,730'''

| '''38,230'''

| '''46,390'''

| '''20,910'''

| '''42,140'''

| '''49,550'''

| '''32,670'''

| '''77,750'''

| '''96,570'''

|-
| '''Alexandria'''

| '''32,840'''

| '''74,100'''

| '''92,470'''

| '''36,220'''

| '''83,700'''

| '''104,270'''

| '''49,990'''

| '''99,500'''

| '''117,580'''

| '''79,360'''

| '''180,090'''

| '''221,390'''

|-
| '''Algiers'''

| '''270'''

| '''620'''

| '''760'''

| '''300'''

| '''700'''

| '''870'''

| '''390'''

| '''810'''

| '''960'''

| '''640'''

| '''1,540'''

| '''1,920'''

|-
| '''Cape Town'''

| '''110'''

| '''310'''

| '''400'''

| '''130'''

| '''360'''

| '''450'''

| '''170'''

| '''410'''

| '''490'''

| '''300'''

| '''800'''

| '''1,010'''

|-
| '''Casablanca'''

| '''350'''

| '''1,150'''

| '''1,520'''

| '''420'''

| '''1,340'''

| '''1,740'''

| '''610'''

| '''1,570'''

| '''1,930'''

| '''1,230'''

| '''3,590'''

| '''4,630'''

|-
| '''Dakar'''

| '''590'''

| '''1,310'''

| '''1,590'''

| '''620'''

| '''1,390'''

| '''1,690'''

| '''760'''

| '''1,530'''

| '''1,800'''

| '''1,180'''

| '''2,880'''

| '''3,610'''

|-
| '''Dar es Salaam'''

| '''880'''

| '''2,100'''

| '''2,600'''

| '''1,050'''

| '''2,440'''

| '''2,970'''

| '''1,360'''

| '''2,760'''

| '''3,250'''

| '''2,140'''

| '''5,120'''

| '''6,360'''

|-
| '''Durban'''

| '''110'''

| '''370'''

| '''470'''

| '''150'''

| '''420'''

| '''530'''

| '''210'''

| '''490'''

| '''590'''

| '''370'''

| '''970'''

| '''1,230'''

|-
| '''Lagos'''

| '''3,680'''

| '''6,790'''

| '''7,950'''

| '''4,200'''

| '''7,660'''

| '''8,930'''

| '''4,920'''

| '''8,270'''

| '''9,420'''

| '''6,750'''

| '''13,820'''

| '''16,730'''

|-
| '''Lome'''

| '''3,230'''

| '''10,480'''

| '''13,460'''

| '''4,280'''

| '''12,580'''

| '''15,780'''

| '''5,980'''

| '''14,430'''

| '''17,380'''

| '''10,720'''

| '''28,580'''

| '''36,010'''

|-
| '''Luanda'''

| '''160'''

| '''380'''

| '''470'''

| '''200'''

| '''440'''

| '''530'''

| '''260'''

| '''510'''

| '''600'''

| '''400'''

| '''910'''

| '''1,130'''

|-
| '''Maputo'''

| '''650'''

| '''1,990'''

| '''2,530'''

| '''700'''

| '''2,080'''

| '''2,620'''

| '''980'''

| '''2,410'''

| '''2,910'''

| '''1,790'''

| '''4,830'''

| '''6,110'''

|-
| '''Aggregate damage'''

'''and risk'''

| '''57,160'''

| '''133,510'''

| '''165,910'''

| '''65,000'''

| '''151,340'''

| '''186,770'''

| '''86,540'''

| '''174,830'''

| '''206,460'''

| '''137,550'''

| '''320,880'''

| '''396,700'''

|}

<div id="9.9.4.1.3" class="h4-container"></div>

<span id="drought"></span>
===== 9.9.4.1.3 Drought =====

<div id="h4-27-siblings" class="h4-siblings"></div>

Although an increase in drought hazard is projected for north and southwest southern Africa with increased global warming (Figure 9.15), central African countries may have the highest drought risk because of high vulnerability and high population growth ( [[#Ahmadalipour--2019|Ahmadalipour et al., 2019]] ). Among continents, Africa contains the second largest population of people living in drylands, which is expected to double by 2050 ( [[#IPCC--2019a|IPCC, 2019a]] ). Continuing current population and GDP growth trends, the extent of urban land in arid zones is projected to increase around 180% in southern Africa, 300% in north Africa and 700% in mid-latitude Africa by 2030 when compared to 2000, without considering climate change ( [[#Güneralp--2015|Güneralp et al., 2015]] ). At 1.5°C warming, urban populations exposed to severe droughts in west Africa are projected to increase (65±34 million) and increase further at 2°C ( [[#IPCC--2018b|IPCC, 2018b]] ; [[#Liu--2018b|Liu et al., 2018b]] ). Risks associated with increases in drought frequency and magnitudes are projected to be substantially larger at 2°C than at 1.5°C for north Africa and southern Africa ( [[#IPCC--2018b|IPCC, 2018b]] ; [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ). Dryland populations exposed (vulnerable) to water stress, heat stress, and desertification are projected to reach 951 (178) million at 1.5°C, 1152 (220) million at 2°C, and 1285 (277) million at 3°C of global warming ( [[#IPCC--2019a|IPCC, 2019a]] ). At global warming of 2°C under a scenario of low population growth and sustainable development (SSP1), the exposed (vulnerable) dryland population is 974 (35) million and for higher population growth and low environmental protections (SSP3) it is 1.27 billion (522 million), a majority of which is in west Africa ( [[#IPCC--2019a|IPCC, 2019a]] ).

<div id="9.9.4.1.4" class="h4-container"></div>

<span id="extreme-heat"></span>
===== 9.9.4.1.4 Extreme heat =====

<div id="h4-28-siblings" class="h4-siblings"></div>

Projections for 173 African cities show that around 25 cities will have over 150 days per year with an apparent temperature above 40.6°C for 1.7°C global warming, increasing to 35 cities for 2.1°C and 65 cities for 4.4°C warming, with west African cities most affected ( [[#Rohat--2019|Rohat et al., 2019]] ). Across Africa, urban population exposure to extreme heat was estimated to be 2 billion person-days per year above 40°C for 1985–2005 (that is the annual average number of days with a maximum temperature above 40.6°C multiplied by the number of people exposed to that temperature), but this is expected to increase to 45 billion person-days for 1.7°C global warming with low population growth (SSP1), and to 95 billion person-days for 2.8°C and medium-high population growth (SSP4) by the 2060s, with increases of 20–52 times 1985–2005 levels by 2080–2100, depending on the scenario ( [[#Rohat--2019|Rohat et al., 2019]] ). West Africa (especially Nigeria) has the highest absolute exposure and southern Africa the least. Considering the urban heat island effect, the more vulnerable populations under 5 and over 64 exposed to heat waves of &gt;15 days over 42°C are projected to increase from 27 million in 2010 to 360 million by 2100 for low population growth (SSP1) with 1.8°C global warming, increasing to 440 million for low population growth (SSP5) with &gt;4°C global warming, with west Africa most affected ( [[#Marcotullio--2021|Marcotullio et al., 2021]] ). This portends increased vulnerability to risk of heat stress in big cities of central, east and west Africa ( ''very high confidence'' ) ( [[#Gasparrini--2015|Gasparrini et al., 2015]] ; [[#Liu--2017|Liu et al., 2017]] ; [[#Rohat--2019|Rohat et al., 2019]] ). Shifting to a low urban population growth pathway is projected to achieve a greater reduction in aggregate exposure to extreme heat for most cities in west Africa, whereas limiting warming through lower emissions pathways achieves greater reductions in exposure in central and east Africa ( [[#Rohat--2019|Rohat et al., 2019]] ).

The African population exposed to compound climate extremes, such as coincident heat waves and droughts or drought followed immediately by extreme rainfall, is projected to increase 47-fold by 2070–2099 compared to 1981–2010 for a scenario with high population growth and 4°C global warming (SSP3/RCP8.5) and only 12-fold for low population growth and 1.6°C global warming (SSP1/RCP2.6), with west, central-east, northeastern and southeastern Africa especially exposed ( [[#Weber--2020|Weber et al., 2020]] ). Coincident heat waves and drought is the compound event to which the most people are projected to be exposed: ~1.9 billion person-events (a 14-fold increase) for SSP1/RCP2.6 and ~7.3 billion person-events (52-fold increase) for SSP3/RCP8.5 ( [[#Weber--2020|Weber et al., 2020]] ).

<div id="9.9.4.2" class="h3-container"></div>

<span id="projected-risks-to-electricity-generation-and-transmission"></span>
==== 9.9.4.2 Projected Risks to Electricity Generation and Transmission ====

<div id="h3-56-siblings" class="h3-siblings"></div>

Climate change poses an increased risk to energy security for human settlements in Africa ( ''high confidence'' ). With burgeoning urban populations and growing economies, sub-Saharan Africa’s electricity needs are growing. The International Energy Agency (IEA) projects total generation capacity in Africa must grow 2.5 times from 244 GW in 2018 to 614 GW by 2040 ( [[#IEA--2019|IEA, 2019]] ). African nations plan to add significant generation capacity from natural gas, hydropower, wind and solar power. Each of these technologies is associated to a varying degree with climate risk.

The long lifespan of hydropower dams exposes them to decades of climatic change risk. There is a wide range of uncertainty around the future climate of Africa’s major river basins, but in several basins, there is the likelihood of increased rainfall variability and a drier climate (see Box 9.5). In countries that rely primarily on hydropower, climate change could have considerable impacts on electricity prices and as a result, consumers’ expenditure ( [[#Sridharan--2019|Sridharan et al., 2019]] ). With increasing societal demands on limited water resources and future climate change, it is expected that there will be an intensification of WEF competition and trade-offs ( ''high confidence'' ) ( [[#9.7|Section 9.7]] ; Box 9.5).

<div id="9.9.4.3" class="h3-container"></div>

<span id="projected-risks-to-road-infrastructure"></span>
==== 9.9.4.3 Projected Risks to Road Infrastructure ====

<div id="h3-57-siblings" class="h3-siblings"></div>

Climate change and SLR will result in high economic costs for road infrastructure in sub-Saharan Africa ( ''medium confidence'' ) ( [[#Chinowsky--2015|Chinowsky et al., 2015]] ). Across Africa as a whole, potential cumulative costs estimates through 2100 range from USD 183.6 billion (with adaptation) to USD 248.3 billion (no adaptation) to repair and maintain existing roads damaged by temperature and precipitation changes directly related to projected climate change (see Figure 9.30) ( [[#Chinowsky--2013|Chinowsky et al., 2013]] ). Climate-related road damage and associated repairs will be a significant financial burden to countries, but to varying degrees according to flood risk, existing road asset liability, topography and rural connectivity, among other factors ( [[#Chinowsky--2015|Chinowsky et al., 2015]] ; [[#Cervigni--2017|Cervigni et al., 2017]] ; [[#Koks--2019|Koks et al., 2019]] ). For example, Mozambique is projected to face estimated annual average costs of USD 123 million for maintaining and repairing roads damaged directly by precipitation and temperature changes from climate change through 2050 in a median climate change scenario for a policy that does not consider climate impacts during road design and construction ( [[#Chinowsky--2015|Chinowsky et al., 2015]] ). Risk of river flooding to bridges in Mozambique under current conditions is estimated to be USD 200 million, equal to 1.5% of its GDP per year, and could rise to USD 400 million per year in the worst-case climate change scenario by 2050 ( [[#Schweikert--2015|Schweikert et al., 2015]] ).

<div id="_idContainer089" class="Figure"></div>

[[File:f6d4ab24d381fd879b95ebe1294c8205 IPCC_AR6_WGII_Figure_9_030.png]]

'''Figure 9.30 |''' '''Projected costs for repair and maintenance of pre-2011 road infrastructure in selected African countries as a result of projected climate-change-related damages due directly to precipitation and temperature changes through to 2100.''' Data sources: [[#Chinowsky--2013|Chinowsky et al. (2013)]] . The analysis was run for 22 SRES climate scenarios and the median, and maximum results of the analyses are represented as proportions of the 2011 GDP of each country.

<div id="9.9.5" class="h2-container"></div>

<span id="adaptation-in-human-settlements-and-for-infrastructure"></span>
=== 9.9.5 Adaptation in Human Settlements and for Infrastructure ===

<div id="h2-38-siblings" class="h2-siblings"></div>

<div id="9.9.5.1" class="h3-container"></div>

<span id="solutions-and-residual-risk-observed-in-human-settlements"></span>
==== 9.9.5.1 Solutions and Residual Risk Observed in Human Settlements ====

<div id="h3-58-siblings" class="h3-siblings"></div>

Autonomous responses to climate impacts in 40 African cities show that excess rainfall is the primary climate driver of adaptation, followed by multi-hazard impacts, with 72% of responses focused on excess rainfall ( [[#Hunter--2020|Hunter et al., 2020]] ). Innovation for adaptation in areas such as home design, social networks, organisations and infrastructure, is evident ( [[#Swanepoel--2019|Swanepoel and Sauka, 2019]] ). Social learning platforms also increase communities’ adaptive capacities and resilience to risk ( [[#Thorn--2015|Thorn et al., 2015]] ).

There is limited evidence of successful, proactively planned climate change adaptation in African cities ( [[#Simon--2015|Simon and Leck, 2015]] ), particularly for those countries highly vulnerable to climate change ( [[#Ford--2014|Ford et al., 2014]] ). Planned adaptation initiatives in African cities since 2006 have been predominantly determined at the national level with negligible participation of lower levels of government ( [[#Ford--2014|Ford et al., 2014]] ). Adaptation action directed at vulnerable populations is also rare ( [[#Ford--2014|Ford et al., 2014]] ). There are emerging examples of cities planned climate adaptation measures, such as those advanced by Durban ( [[#Roberts--2010|Roberts, 2010]] ), Cape Town ( [[#Taylor--2016|Taylor et al., 2016]] ) and Lagos ( [[#Adelekan--2016|Adelekan, 2016]] ). There are also examples of community-led projects such as those in Maputo ( [[#Broto--2015|Broto et al., 2015]] ), which have seen meaningful help from a range of policy networks, dialogue forums and urban learning labs ( [[#Pasquini--2014|Pasquini and Cowling, 2014]] ; [[#Shackleton--2015|Shackleton et al., 2015]] ). These researched cities can be lighthouses for wider exchange and the basis for a deeper synthesis of evidence ( [[#Lindley--2019|Lindley et al., 2019]] ). However, planned adaptation progress is slow, especially in west and central Africa ( [[#Tiepolo--2014|Tiepolo, 2014]] ).

Ecosystem-based approaches are also being deployed in mitigating and adapting to climate change, with demonstrated long-term health, ecological and social co-benefits ( [[#9.6.4|Section 9.6.4]] ; [[#Swanepoel--2019|Swanepoel and Sauka, 2019]] ). The cost–benefit analysis of nature-based solutions, compared to purely grey infrastructure initiatives, is discussed in [[IPCC:Wg2:Chapter:Chapter-6|Chapter 6]] ( [[IPCC:Wg2:Chapter:Chapter-6#6.3.3|Section 6.3.3]] ). Nature-based solutions can also lengthen the life of existing built infrastructure ( [[#du%20Toit--2018|du Toit et al., 2018]] ). Since 2014, an increasing number of EbA projects involving the restoration of mangrove, wetland and riparian ecosystems have been initiated across Africa, a majority of which address water-related climate risks (Table 9.9).

'''Table 9.9 |''' Examples of ecosystem-based adaptations to climate impacts in African cities.

{| class="wikitable"
|-
! Project

! City

! Ecosystem-based Adaptation

! Reference

|-
| Green Urban Infrastructure

| Beira (Mozambique)

| Mitigating against increased flood risks through restoration of mangrove and other natural habitats along the Chiveve river and the development of urban green spaces.

| [[#IPCC--2019a|IPCC (2019a)]] ; [[#CES%20Consulting%20Engineers%20Salzgitter%20GmbH%20and%20Inros%20Lackner%20SE--2020|CES Consulting Engineers Salzgitter GmbH and Inros Lackner SE (2020)]]

|-
| The Msimbazi Opportunity Plan (MOP) 2019–2024

| Dar es Salaam, Tanzania

| Enhancing urban resilience to flood risk by reducing flood hazard, and reducing people, properties and critical infrastructure exposed to flood hazard.

| Croitoru et al. (2019)

|-
| Tanzania Ecosystem-based Adaptation

| Dar es Salaam and five coastal districts, Tanzania

| Rehabilitation of over 3000 ha of climate-resilient mangrove species.

| [[#UNEP--2019|UNEP (2019)]]

|-
| Building Resilience in the Coastal Zone through Ecosystem-based Approaches to Adaptation

| Maputo, Mozambique

| Restoration of mangrove and riparian ecosystems for flood control and protection from coastal flooding enhanced water supply.

| [[#GEF--2019|GEF (2019)]]

|-
| Addressing Urgent Coastal Adaptation Needs and Capacity Gaps in Angola

| Five coastal communities in Angola

| Restoration of 561 ha of wetland, mangroves and other ecological habitats to promote flood defence and mitigate the threat of drought.

| [[#UNEP--2020|UNEP (2020)]]

|-
| Green City Kigali

2016

| Kigali, Rwanda

| Planned neighbourhood of 600 ha, integrating green building and design, efficient and renewable energy, recycling and inclusive living.

| [[#SWECO--2019|SWECO (2019)]]

|-
| Urban Natural Assets for Africa—Rivers for Life

| Kampala, Uganda

| Preservation of natural buffers to enhance the protective functions offered by natural ecosystems that support disaster resilience benefit.

| [[#World%20Bank--2015|World Bank (2015)]]

|}

For green infrastructure to be successful, however, sustainable landscapes and regions require both stewardship and management at multiple levels of governance and social scales ( [[#Brink--2016|Brink et al., 2016]] ).

Currently, planned climate change adaptation to coastal hazards in Africa’s large coastal cities has mainly been achieved through expensive coastal engineering efforts such as sea walls, revetments, breakwaters, spillways, dikes and groynes. Examples are found in west Africa ( [[#Adelekan--2016|Adelekan, 2016]] ; [[#Alves--2020|Alves et al., 2020]] ). Beach nourishment efforts have also been undertaken in Egypt, Banjul and Lagos ( [[#Frihy--2016|Frihy et al., 2016]] ; [[#Alves--2020|Alves et al., 2020]] ). However, the use of vegetated coastal ecosystems presents greater opportunities for African cities because of the lower costs ( [[#Duarte--2013|Duarte et al., 2013]] ).

Most (&gt;80%) of Africa’s large coastal cities have no adaptation policies and, where available, these are mostly, except for South Africa, dominated by national plans ( [[#Olazabal--2019|Olazabal et al., 2019]] ). Coastal adaptation actions minimally consider socioeconomic projections and are not at all aligned with future climate scenarios and risks, which is highly limiting for adaptation planning ( [[#Olazabal--2019|Olazabal et al., 2019]] ).

<div id="9.9.5.2" class="h3-container"></div>

<span id="anticipated-adaptation-and-residual-risk-for-human-settlements"></span>
==== 9.9.5.2 Anticipated Adaptation and Residual Risk for Human Settlements ====

<div id="h3-59-siblings" class="h3-siblings"></div>

Africa’s smaller towns and cities have received far less scholarly and policy development attention for adaptation ( [[#Clapp--2017|Clapp and Pillay, 2017]] ; [[#White--2019|White and Wahba, 2019]] ). Smaller towns also have less ability to partner effectively with private entities for adaptation initiatives ( [[#Wisner--2015|Wisner et al., 2015]] ). Political will to address climate change and information flows between key stakeholders, professional and political decision makers may be easier to establish in smaller cities than in the megacity context ( [[#Wisner--2015|Wisner et al., 2015]] ).

Exposure and vulnerability are particularly acute in informal areas, making coordinated adaptation challenging. Yet, there is growing recognition of the potential for bottom-up adaptation that embraces informality in order to more effectively reduce risk (Figure 9.31; [[#Taylor--2021a|Taylor et al., 2021a]] ). This can provide an opportunity for change towards more risk-sensitive urban development and transformative climate adaptation ( [[#Leck--2018|Leck et al., 2018]] ). Addressing social vulnerability is particularly important for ensuring the resilience of populations at risk. Improved monitoring, modelling and communication of climate risks is needed to reduce the impacts of climate hazards ( [[#Tramblay--2020|Tramblay et al., 2020]] ; [[#Cole--2021a|Cole et al., 2021a]] ).

<div id="_idContainer092" class="Figure"></div>

[[File:a9afeb3beb4a5f690865789d2c2e6514 IPCC_AR6_WGII_Figure_9_031.png]]

'''Figure 9.31 |''' '''Key elements of adaptation in informal settlements in Africa.''' Adapted from Thorn et al. (2015); [[#Fedele--2019|Fedele et al. (2019)]] ; [[#Satterthwaite--2020|Satterthwaite et al. (2020)]] .

<div id="9.9.5.3" class="h3-container"></div>

<span id="anticipated-adaptation-for-transport-systems-in-africa"></span>
==== 9.9.5.3 Anticipated Adaptation for Transport Systems in Africa ====

<div id="h3-60-siblings" class="h3-siblings"></div>

Higher costs will be incurred to maintain and repair damages caused to existing roads as a result of climate change for countries with no adaptation policy for transport infrastructure ( ''very high confidence'' ) ( [[#Chinowsky--2013|Chinowsky et al., 2013]] ; [[#Cervigni--2017|Cervigni et al., 2017]] ; [[#Koks--2019|Koks et al., 2019]] ). Countries with a greater percentage of unpaved roads will, however, incur higher economic costs through adaptation policy when compared to no adaptation policy ( [[#Cervigni--2017|Cervigni et al., 2017]] ).

Adaptation measures in the transport sector have focused on the climate resilience of road infrastructure. Modelling suggests that proactive adaptation of road designs to account for temperature increases is a ‘no regret’ option in all cases, but accounting for precipitation increases should be assessed on a case-by-case basis ( ''medium confidence'' ) ( [[#Cervigni--2017|Cervigni et al., 2017]] ). African governments will need climate adaptation financing options to meet the higher capital requirements of resilient road infrastructure interventions ( [[#Hearn--2016|Hearn, 2016]] ).

Under the Nationally Appropriate Mitigation Action programme, investments in public transport and transit-oriented development are highlighted as desired mitigation–adaptation interventions within cities of South Africa, Ethiopia and Burkina Faso (UNFCCC, 2020). These interventions simultaneously reduce the vulnerability of low-income residents to climate shocks, prevent lock-ins into carbon-intensive development pathways and reduce poverty ( ''high confidence'' ) ( [[#Hallegatte--2016|Hallegatte et al., 2016]] ; [[#Rozenberg--2019|Rozenberg et al., 2019]] ). The combined mitigation–adaptation interventions in the land use transport systems of African cities are also expected to have sufficient short-term co-benefits (reducing air pollution, congestion and traffic fatalities) to be ‘no regret’ investments ( ''very high confidence'' ) ( [[#Hallegatte--2016|Hallegatte et al., 2016]] ; [[#Rozenberg--2019|Rozenberg et al., 2019]] ). Only eight African countries have transport-specific adaptation measures in their NDCs ( [[#Nwamarah--2018|Nwamarah, 2018]] ). Five African countries have submitted NAPs (Table 9.10).

'''Table 9.10 |''' Transport sector references in the National Adaptation Plans (NAPs) of five African countries. Source: [[#Government%20of%20Burkina%20Faso--2015|Government of Burkina Faso (2015)]] ; [[#Government%20of%20Cameroon--2015|Government of Cameroon (2015)]] ; [[#Government%20of%20Togo--2016|Government of Togo (2016)]] ; [[#Government%20of%20Kenya--2017|Government of Kenya (2017)]] ; [[#Government%20of%20Ethiopia--2019|Government of Ethiopia (2019)]] .

{| class="wikitable"
|-
! rowspan="2"| Country

! rowspan="2"| Identify climate change impacts

! rowspan="2"| Promote transport as a disaster risk reduction measure

! colspan="4"| Transport-specific adaptation measures

|-
! Climate-resilient design standards

! Promote public transport

! Promote non-motorised transport

! Urban land use planning

|-
| Burkina Faso

| X

|
| X

| X

|
| X

|-
| Cameroon

|
| X

| X

|
|-
| Ethiopia

| X

| X

| X

| X

|
|-
| Kenya

| X

|
|-
| Togo

|
| X

| X

|
|}

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==== 9.9.5.4 Projected Adaptation for Electricity Generation and Transmission in Africa ====

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Most electricity infrastructure in Africa has been designed to account for historical climatic patterns. Failure to consider future climate scenarios in power system planning increases the climate risk facing infrastructure and supplies. Yet, energy demand for cooling over Africa, for example, is expected to increase, with a potential increase in heat stress, population growth and rapid urbanisation to 1.2% of total final energy demand by 2100 compared to 0.4% in 2005 ( [[#Parkes--2019|Parkes et al., 2019]] ). Integrated energy system costs from increased demand for cooling to mitigate heat stress are projected to accumulate from 2005 to USD 51.3 billion by 2035 at 2°C and to USD 486.5 billion by 2076 at 4°C global warming ( [[#Parkes--2019|Parkes et al., 2019]] ).

For hydropower, adaptations to different climate conditions can be made at the level of the power plant, turbine size and reservoir storage capacities, and can be adjusted to projected hydrological patterns ( [[#Lempert--2015|Lempert et al., 2015]] ). At the river basin level, integrated water resource management practices can be implemented across sectors that compete for the same water resources ( [[#Howells--2013|Howells et al., 2013]] ). At the power system level, the energy mix and the protocol through which different power plants are dispatched can be adapted to different climate scenarios ( [[#Spalding-Fecher--2017|Spalding-Fecher et al., 2017]] ; [[#Sridharan--2019|Sridharan et al., 2019]] ).

Given the uncertainty around future hydroclimate conditions, hydropower development decisions carry risk of ‘regrets’ (that is, damages or missed opportunities) when a different climate than was expected materialises. ‘Robust adaptation’ refers to an adaptation strategy that balances risks across different climate scenarios (Cross-Chapter Box DEEP in Chapter 17; [[#Cervigni--2015|Cervigni et al., 2015]] ). Development bank lending principles require consideration of the regional picture and interactions with other developments along a river when they determine the social and environmental impacts of the proposed hydropower project. However, these principles often do not explicitly consider climate change, so the risk of recurring drought-induced hydropower shortages could be missed (Box 9.5).

Lastly, given the degree to which hydropower competes with other sectors and ecosystems for the same water resources, it is critical that hydropower planning and adaptation does not occur in isolation. As discussed in [[#9.7|Section 9.7]] , it must be part of an integrated water management system that balances the needs of different water-reliant sectors with other societal and ecological demands under increasingly variable climate and hydrological conditions ( [[#9.7.3|Section 9.7.3]] ).

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