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

== 9.11 Economy, Poverty and Livelihoods ==

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<span id="observed-impacts-of-climate-change-on-african-economies-and-livelihoods"></span>
=== 9.11.1 Observed Impacts of Climate Change on African Economies and Livelihoods ===

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<span id="economic-output-and-growth"></span>
==== 9.11.1.1 Economic Output and Growth ====

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Increased average temperatures and lower rainfall have reduced economic output and growth in Africa, with larger negative impacts than other regions of the world ( [[#Abidoye--2015|Abidoye and Odusola, 2015]] ; [[#Burke--2015a|Burke et al., 2015a]] ; [[#Acevedo--2017|Acevedo et al., 2017]] ; [[#Kalkuhl--2020|Kalkuhl and Wenz, 2020]] ). In one estimate, GDP per capita is on average 13.6% lower for African countries than it would be if human-caused global warming since 1991 had not occurred ( [[#Diffenbaugh--2019|Diffenbaugh and Burke, 2019]] ), although impacts vary substantially across countries (see Figure 9.37). As such, global warming has increased economic inequality between temperate, northern Hemisphere countries and those in Africa ( [[#Diffenbaugh--2019|Diffenbaugh and Burke, 2019]] ). Warming also leads to differential economic damages within Africa ( [[#Baarsch--2020|Baarsch et al., 2020]] ). One estimate found a 1°C increase in 20-year average temperature reduced GDP growth by 0.67 percentage points, with the greatest impacts in Central African Republic, DRC and Zimbabwe ( [[#Abidoye--2015|Abidoye and Odusola, 2015]] ). Changes in rainfall patterns also influence individual and national incomes. Had total rainfall not declined between 1960 and 2000, the gap between African GDP and that of the rest of the developing world would be 15–40% smaller than today, with the largest impacts in countries heavily dependent on agriculture and hydropower ( [[#Barrios--2010|Barrios et al., 2010]] ).

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[[File:3e92195da20fcb17e8b468e6d8200654 IPCC_AR6_WGII_Figure_9_037.png]]

'''Figure 9.37 |''' '''Observed aggregate economic impacts and projected risks from climate change in Africa.'''

'''(a)''' Estimated effect of human-caused climate change on GDP per capita for 48 African countries between 1991 and 2010.

'''(b)''' Projected effect on GDP per capita of global warming of ~4°C by 2100 compared to economic growth with no further global warming after 2010.

'''(c)''' Projected percentage increase in GDP per capita of holding global warming to 1.5°C rather than 2°C above pre-industrial level.

'''(d)''' Probability of realising any economic benefits by holding warming to 1.5°C versus 2°C. Data sources: Burke et al. (2015b); (2018a); [[#Diffenbaugh--2019|Diffenbaugh and Burke (2019)]] .

Aggregate macroeconomic impacts manifest through many channels ( [[#Carleton--2016|Carleton et al., 2016]] ). Macroeconomic evidence suggests aggregate impacts occurred largely through losses in agriculture with a smaller role for manufacturing ( [[#Barrios--2010|Barrios et al., 2010]] ; [[#Burke--2015b|Burke et al., 2015b]] ; [[#Acevedo--2017|Acevedo et al., 2017]] ). Sector-specific analyses confirm that declines in productivity of food crops, commodity crops and overall land productivity contribute to lower macroeconomic performance under rising temperatures ( [[#Schlenker--2010|Schlenker and Lobell, 2010]] ; [[#Bezabih--2011|Bezabih et al., 2011]] ; [[#Jaramillo--2011|Jaramillo et al., 2011]] ; [[#Lobell--2011|Lobell et al., 2011]] ; [[#Adhikari--2015|Adhikari et al., 2015]] ). Labour supply and productivity declines in manufacturing, industry, services and daily wage labour have been observed in other regions ( [[#Graff%20Zivin--2014|Graff Zivin and Neidell, 2014]] ; [[#Somanathan--2015|Somanathan et al., 2015]] ; [[#Day--2019|Day et al., 2019]] ; [[#Nath--2020|Nath, 2020]] ) and contribute to aggregate economic declines, countering aggregate poverty reduction strategies and other SDGs ( [[#Satterthwaite--2017|Satterthwaite and Bartlett, 2017]] ; [[#Day--2019|Day et al., 2019]] ). In a case study of a rural town in South Africa, over 80% of businesses (both formal and informal) lost over 50% of employees and revenue due to agricultural drought ( [[#Hlalele--2016|Hlalele et al., 2016]] ). Drought and extreme heat events have also reduced tourism revenues in Africa ( [[#9.6.3|Section 9.6.3]] ). Infrastructure damage and transport disruptions from adverse climate events reduce access to services and growth opportunities ( [[#Chinowsky--2014|Chinowsky et al., 2014]] ). In global data sets including Africa, tropical cyclones have been shown to have large and long-lasting negative impacts on GDP growth ( [[#Hsiang--2014|Hsiang and Jina, 2014]] ).

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<span id="human-capital-development-and-education"></span>
==== 9.11.1.2 Human Capital Development and Education ====

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Investments in human capital, particularly education, are critical for socioeconomic development and poverty reduction providing valuable skills and expanding labour market opportunities. Much progress has been made in improving education access, however, in sub-Saharan Africa, 32% of children, adolescents and youth (~97 million people) remain out of school ( [[#UNESCO%20Institute%20of%20Statistics--2018|UNESCO Institute of Statistics, 2018]] ). Climate variability and change can undermine educational attainment with negative impacts on later life earning potential and adaptive capacity to future climate change (Figure 9.11; [[#Lutz--2014|Lutz et al., 2014]] ).

Several studies indicate that experiencing low rainfall, warming temperatures or extreme weather events reduce education attainment and that future climate change may reduce children’s school participation, particularly for agriculturally dependent and poor urban households. In west and central Africa, experiencing lower-than-average rainfall during early life is associated with up to 1.8 fewer years of completed schooling in adolescence, while more rainfall and milder temperatures during the main agricultural season are positively associated with educational attainment for boys and girls in rural Ethiopia ( [[#Randell--2016|Randell and Gray, 2016]] ; [[#Randell--2019|Randell and Gray, 2019]] ). In Uganda, low rainfall reduced primary school enrolment by 5% for girls ( [[#Björkman-Nyqvist--2013|Björkman-Nyqvist, 2013]] ), and in Malawi, ''in utero'' drought exposure was associated with delayed school entry among boys ( [[#Abiona--2017|Abiona, 2017]] ). In rural Zimbabwe, experiencing drought conditions during the first few years of life was associated with fewer grades of completed schooling in adolescence, which translates into a 14% reduction in lifetime earnings ( [[#Alderman--2006|Alderman et al., 2006]] ). In Cameroon, warming temperatures have negatively affected plantain yields, which in turn is linked to lower educational attainment ( [[#Fuller--2018|Fuller et al., 2018]] ). One suggested mechanism underlying the relationship between climate and schooling is that adverse climatic conditions can reduce income among farming households, leading them to pull children out of school ( [[#Randell--2016|Randell and Gray, 2016]] ; [[#Marchetta--2019|Marchetta et al., 2019]] ). Other potential mechanisms are poor harvests from droughts or supply interruptions from extreme weather events leading to undernutrition among young children, negatively affecting cognitive development and schooling potential ( [[#Alderman--2006|Alderman et al., 2006]] ; [[#Bartlett--2008|Bartlett, 2008]] ).

More research is needed on climate change impacts on education in Africa. This information can help ensure families keep children in school amid climate-related income shocks. For example, in Mexico, a conditional cash transfer programme mitigated the negative effect of natural disasters on school attendance ( [[#de%20Janvry--2006|de Janvry et al., 2006]] ).

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<span id="projected-risks-of-climate-change-for-african-economies-and-livelihoods"></span>
=== 9.11.2 Projected Risks of Climate Change for African Economies and Livelihoods ===

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Future warming will have negative consequences for economic growth in Africa, relative to a future without additional climate change and assuming current levels of adaptation ( ''high confidence'' ) ( [[#Dell--2012|Dell et al., 2012]] ; 2015a; [[#Burke--2015b|Burke et al., 2015b]] ; [[#Acevedo--2017|Acevedo et al., 2017]] ; [[#Baarsch--2020|Baarsch et al., 2020]] ). Statistically based empirical analyses project that global warming of 2.3°C by 2050 could lower GDP per capita across sub-Saharan Africa by 12% (SSP2) ( [[#Baarsch--2020|Baarsch et al., 2020]] ) and 80% for warming &gt;4°C by 2100 (SSP5, 75% for MENA) ( [[#Burke--2015b|Burke et al., 2015b]] ). Depending on the future socioeconomic scenario, this could increase global inequality and leave some African countries poorer than at present ( [[#Burke--2015b|Burke et al., 2015b]] ). Inequalities between African countries are projected to widen under climate change, with negative impacts estimated to be largest in west and east Africa ( [[#Baarsch--2020|Baarsch et al., 2020]] ). While negative impacts across African economies are highly ''likely'' under climate change, precise magnitudes are debated in the literature. Alternative statistical analyses suggest a 12% reduction of GDP per capita by 2100 under RCP8.5 across African countries relative to a future without climate change ( [[#Kahn--2021|Kahn et al., 2021]] ), while computable general equilibrium models generate smaller damages as well, ranging from 3.8% reduction across sub-Saharan Africa in 2060 under warming of 2.5°C ( [[#Dellink--2019|Dellink et al., 2019]] ) to 12% across all of Africa in 2100 under warming of 5°C (SSP4) ( [[#Takakura--2019|Takakura et al., 2019]] ).

Substantial avoided economic damages to African countries are projected from ambitious, near-term global mitigation limiting global warming well below 2°C above pre-industrial levels ( ''high confidence'' ). Increased economic damage forecasts for Africa under high emissions scenarios start diverging rapidly from low emissions scenarios by the 2030s ( [[#Baarsch--2020|Baarsch et al., 2020]] ). Across nearly all African countries, GDP per capita is projected to be at least 5% higher by 2050 and 10–20% higher by 2100 if global warming is held to 1.5°C versus 2°C ( [[#Burke--2018a|Burke et al., 2018a]] ; [[#Baarsch--2020|Baarsch et al., 2020]] ) (Figure 9.37). The probability of this positive gain to GDP per capita from achieving 1.5°C versus 2°C is reported as close to 100% ( [[#Burke--2018a|Burke et al., 2018a]] ). While these estimates rely on temperature and rainfall-driven damages, SLR also poses a risk for Africa. By 2050, damages from SLR across sub-Saharan Africa could reach 2–4% of GDP, depending on the socioeconomic, adaptation and emissions scenario ( [[#Parrado--2020|Parrado et al., 2020]] ).

Heat stress is projected to reduce working hours and work capacity under climate change, with among the largest declines in sub-Saharan Africa and for workers in vulnerable occupation groups, such as those working outdoors ( [[#Kjellstrom--2014|Kjellstrom et al., 2014]] ; 2016; [[#de%20Lima--2021|de Lima et al., 2021]] ; Chapter 5). Global warming of 3°C is projected to reduce labour capacity in agriculture by 30–50% in sub-Saharan Africa (relative to the baseline in 1986–2005) ( [[#de%20Lima--2021|de Lima et al., 2021]] ). These effects lead to substantial aggregate losses, for example, in west Africa, labour productivity impacts under a 3°C temperature increase are estimated to cost up to 8% of GDP ( [[#Roson--2016|Roson and Sartori, 2016]] ). Manufacturing productivity across Africa is projected to decline under RCP8.5 by 0–15% by 2080–2099, with the largest effects in the DRC, Ethiopia, Somalia, Mozambique and Malawi ( [[#Nath--2020|Nath, 2020]] ).

Large risks to road, rail and water infrastructure are projected from climate change with substantial economic cost implications (see [[#9.9.3|Section 9.9.3]] ; Box 9.5).

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<span id="informality"></span>
=== 9.11.3 Informality ===

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Aggregate GDP data capture formal economic activity but informal employment is the main source of employment in Africa, accounting for 85.8% of all employment (71.9%, excluding agriculture), which is 21.4% higher than the global average ( [[#ILO--2018b|ILO, 2018b]] ). Estimates of national levels of informal employment range from 30% in South Africa, to 94.6% in Burkina Faso ( [[#ILO--2018b|ILO, 2018b]] ), with high variability within countries such as South Africa and Nigeria ( [[#Etim--2020|Etim and Daramola, 2020]] ). Informal employment is a greater source of employment for women than for men in sub-Saharan Africa and young and old have especially high rates of informal employment: 94.9% of persons between ages 15 and 24 in employment and 96% of persons aged 65 and older ( [[#ILO--2018b|ILO, 2018b]] ).

Informal sector impacts are omitted from GDP-based impacts projections. Yet, informal sector activity and small to medium-sized enterprises can be highly exposed to climate extremes, as they are often located in low-lying areas, coastal areas, sloped or other hazardous zones ( [[#Thorn--2015|Thorn et al., 2015]] ; [[#Satterthwaite--2020|Satterthwaite et al., 2020]] ). Businesses and individuals in the informal sector, including construction workers, domestic workers, street vendors and transport workers, often cannot operate during climate shocks due to interruptions in transportation and commodity flows and, without the ability to insure against risk, struggle to recover assets from extreme events such as flooding, landslides and waterlogging ( [[#Chen--2014|Chen, 2014]] ; [[#Thorn--2015|Thorn et al., 2015]] ; [[#Roy--2018a|Roy et al., 2018a]] ). Women are overrepresented in the more poorly remunerated sections of the informal economy ( [[#Satterthwaite--2020|Satterthwaite et al., 2020]] ).

There is scope for governments to better harness the role of the informal sector in mitigation and adaptation ( [[#Douxchamps--2015|Douxchamps et al., 2015]] ; [[#Satterthwaite--2020|Satterthwaite et al., 2020]] ). Multi-level governance that includes informal service providers, such as informal water and sanitation networks, into planned adaptation programmes can increase climate resilience, in part because these networks can respond with more flexibility than hard infrastructure projects ( [[#Satterthwaite--2020|Satterthwaite et al., 2020]] ; [[#Peirson--2021|Peirson and Ziervogel, 2021]] ). Climate risk is often concentrated in urban informal settlements ( [[#9.9.4|Section 9.9.4]] ). Here, informal land markets influence development patterns and can help ensure adherence to building codes to ensure safety of informally built structures at high risks of landslides and floods and enforce compliance with regulations relating to planning and land use ( [[#Thorn--2015|Thorn et al., 2015]] ; [[#Satterthwaite--2020|Satterthwaite et al., 2020]] ). Improving land management practices of charcoal producers and artisanal gold miners, combined with appropriate alternative livelihood and energy sources, can reduce emissions and increase resilience (e.g., reduce erosion and sedimentation, increase water infiltration) and benefit health ( [[#Atteridge--2013|Atteridge, 2013]] ; [[#Paz--2015|Paz et al., 2015]] ; [[#Macháček--2019|Macháček, 2019]] ; [[#Barenblitt--2021|Barenblitt et al., 2021]] ; [[#Eniola--2021|Eniola, 2021]] ). Providing concessional loans, commercial financing or equity investment to informal brick makers can boost delivery of low emission social housing while the use of crop residues or renewable energy for brick making can replace wood biomass and reduce pressure on forests ( [[#Alam--2006|Alam, 2006]] ; [[#Paz--2015|Paz et al., 2015]] ).

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<span id="climate-change-adaptation-to-reduce-vulnerability-poverty-and-inequality"></span>
=== 9.11.4 Climate Change Adaptation to Reduce Vulnerability, Poverty and Inequality ===

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High temperature-related income losses have been observed in both low- and high-income countries, suggesting optimistic economic development trajectories may not substantially reduce climate change impacts on aggregate economic performance in Africa ( ''low confidence'' ) ( [[#Burke--2015b|Burke et al., 2015b]] ; [[#Deryugina--2017|Deryugina and Hsiang, 2017]] ; [[#Henseler--2019|Henseler and Schumacher, 2019]] ). Nevertheless, climate change impacts on poverty in Africa will depend on how socioeconomic development unfolds over the coming decades ( ''medium confidence'' ) ( [[#Rozenberg--2015|Rozenberg and Hallegatte, 2015]] ; [[#Hallegatte--2017|Hallegatte and Rozenberg, 2017]] ; [[#Henseler--2019|Henseler and Schumacher, 2019]] ). Climate change by 2030 is projected to push 39.7 million Africans into extreme poverty [[#footnote-000|3]] under a baseline scenario of delayed and non-inclusive growth, with food prices acting as the dominant channel of impact, but this number is cut roughly in half under an inclusive economic growth scenario ( [[#Rozenberg--2015|Rozenberg and Hallegatte, 2015]] ; [[#Hallegatte--2017|Hallegatte and Rozenberg, 2017]] ; [[#Jafino--2020|Jafino et al., 2020]] ).

People in Africa are disproportionately employed in highly climate-sensitive sectors: 55–62% of the sub-Saharan African workforce is employed in agriculture and, although between 90–95% of cropland is rainfed ( [[#Woodhouse--2017|Woodhouse et al., 2017]] ; [[#ILO--2018a|ILO, 2018a]] ; International Institute of Water Management, 2019; [[#World%20Bank--2020c|World Bank, 2020c]] ), there has been an expansion of small-scale ‘farmer-led irrigation’ ( [[#Woodhouse--2017|Woodhouse et al., 2017]] ). Agricultural GDP also appears more strongly affected by increasing temperatures than non-agricultural GDP, implying livelihood diversification out of agriculture may help minimise future economic damage ( [[#Bezabih--2011|Bezabih et al., 2011]] ; [[#Burke--2015b|Burke et al., 2015b]] ; [[#Acevedo--2017|Acevedo et al., 2017]] ; [[#Deryugina--2017|Deryugina and Hsiang, 2017]] ), although such workforce reallocation requires careful management and planning depending on the overall livelihood portfolios, type of farmer and profitability ( [[#Stringer--2020|Stringer et al., 2020]] ). De-agrarianisation can feed urbanisation, which may exacerbate inequality within and between countries ( [[#Stringer--2020|Stringer et al., 2020]] ).

Changes in trade patterns may help mitigate projected aggregate economic losses by reallocating agricultural production abroad and encouraging economic diversification toward less affected sectors. Temperature increases have been shown to lower agriculture and manufacturing exports with especially large declines in poor countries ( [[#Jones--2010|Jones and Olken, 2010]] ; [[#Roberts--2013|Roberts and Schlenker, 2013]] ). Further, imports of agricultural products are projected to rise across most of Africa by 2080–2099 under a high emissions scenario (RCP8.5), with increases ranging from ~30% of GDP in the Central African Republic to ~5% of GDP in South Africa and Nigeria, although some countries will experience increases in net agricultural exports ( [[#Nath--2020|Nath, 2020]] ). While these reallocation effects may be large, current evidence is mixed regarding whether such adjustment of production will dampen or amplify overall social costs of climate change in Africa ( [[#Costinot--2012|Costinot et al., 2012]] ; [[#Bren%20d’Amour--2016|Bren d’Amour et al., 2016]] ; [[#Wenz--2016|Wenz and Levermann, 2016]] ; [[#Nath--2020|Nath, 2020]] ), as food prices are projected to rise by 2080–2099 across all African countries under a scenario with high challenges to mitigation and adaptation (SSP3 and RCP8.5), with the largest price effects (up to 120%) experienced in Chad, Niger and Sudan ( [[#Nath--2020|Nath, 2020]] ). Moreover, reallocating production of agriculture abroad could be maladaptive if it leads to decline or replacement of traditional sectors by industrial and service sectors, which could lead to land abandonment, food insecurity and loss of traditional practices and cultural heritage ( [[#Thorn--2020|Thorn et al., 2020]] ; [[#Gebre--2021|Gebre and Rahut, 2021]] ; [[#Nyiwul--2021|Nyiwul, 2021]] ).

African countries have high inequality: the average within-country share of income accruing to the top 10% of households was estimated at 50% for 2019 ( [[#Robilliard--2020|Robilliard, 2020]] ). However, analysis of INDCs across 54 African countries suggests current climate policies do not, on average, target social inequality in energy, water and food security; proposed mitigation and adaptation actions fell about 23% for every 1% rise in social inequality across these sectors ( [[#Nyiwul--2021|Nyiwul, 2021]] ). In contrast, adaptation actions can be designed in ways that actively work towards reducing inequality, whether gender, income, employment, education or otherwise ( [[#Andrijevic--2020|Andrijevic et al., 2020]] ).

In rural Africa, poor and female-headed households face greater livelihood risks from climate hazards ( ''high confidence'' ). Women often constitute a high proportion of the informal workforce and are also more ''likely'' to be unemployed than men ( [[#ILO--2018a|ILO, 2018a]] ). These factors leave women, and particularly female-headed households, at greater risk of poverty and food insecurity from climate hazards. Controlling for multiple factors, income of female-headed households in agricultural districts in South Africa is more vulnerable to precipitation variability than those headed by men ( [[#Davidson--2016|Davidson, 2016]] ; [[#Flatø--2017|Flatø et al., 2017]] ). Across nine countries in east and west Africa women tend to control smaller plots of land that is often of poorer quality, have less access to inputs such as fertilizer, tools and improved seeds, have lower educational attainment and benefit less from extension services, government agencies and non-governmental organisations ( [[#Perez--2015|Perez et al., 2015]] ). Gender assessments prior to adaptation programmes can identify disparities in division of labour and income and socio-cultural norms, hindering women from holding leadership positions or determining livelihood and resource-use activities, thereby helping ensure equitable benefits from livelihood diversification and improving women’s working conditions ( [[#ILO--2018a|ILO, 2018a]] ). Gender-responsive policy instruments can measure success using sex-disaggregated data to monitor impact and meaningful participation in decision making ( [[#GCF--2018b|GCF, 2018b]] ).

Exposure to climate hazards can trap poorer households in a cycle of poverty ( [[#Dercon--2011|Dercon and Christiaensen, 2011]] ; [[#Sesmero--2018|Sesmero et al., 2018]] ) and poor people in Africa are often more exposed to climate hazards than non-poor people. For example, poor people live in hotter areas in Nigeria and in multiple African countries, poor households are more exposed to flooding ( [[#9.9.2|Section 9.9.2]] ; [[#Hallegatte--2016|Hallegatte et al., 2016]] ). Daily wage labourers and residents of urban informal settlements are vulnerable to heat stress because of the urban heat island effect combined with congestion, little shade and ventilation ( [[#Bartlett--2008|Bartlett, 2008]] ). Climate change can negatively affect household poverty through price spikes, destroying assets or ability to invest in new assets and reducing productivity ( [[#Hallegatte--2016|Hallegatte et al., 2016]] ) with important impact pathways operating through agriculture, ecosystem functioning and health (Sections 9.6; 9.8; 9.10; Chapters 5; 7; 8). Non-poor people can lose more in absolute terms from climate shocks because of having more assets and higher incomes, but in relative terms, poor people often lose more than the non-poor. These relative losses matter most for livelihoods and welfare ( [[#Hallegatte--2016|Hallegatte et al., 2016]] ).

In Malawi, wealthier households were able to maintain more diversified livelihoods, buffering them from extreme weather-related income losses ( [[#Sesmero--2018|Sesmero et al., 2018]] ). Poorer households have limited access to resources such as savings, credit, irrigation technologies and insurance, which can lead to larger crop and other income losses from climate hazards, preventing investments to improve resilience to future climate shocks ( [[#Castells-Quintana--2018|Castells-Quintana et al., 2018]] ). Poor households may reduce risk or aid recovery by cooperating with other households in their community to adapt collectively to climate change, for example, through informal insurance networks ( [[#Paul--2016|Paul et al., 2016]] ; [[#Wuepper--2018|Wuepper et al., 2018]] ). Prioritising poor households for interventions including social protection, EbA, universal healthcare, climate-smart buildings and agriculture, flexible work hours under extreme heat and early warning systems will increase adaptation to climate shocks ( [[#9.6.4|Section 9.6.4]] ; Chapter 6; [[#Angula--2014|Angula and Menjono, 2014]] ; [[#Moosa--2014|Moosa and Tuana, 2014]] ; [[#Hallegatte--2016|Hallegatte et al., 2016]] ; [[#Day--2019|Day et al., 2019]] ). Pro-poor policies that link mitigation and adaptation, such as using renewable energy to increase rural electrification or using revenues from a carbon tax, combined with international financial support to increase social assistance, could support sustainable eradication of poverty under near-term climate change ( [[#Hallegatte--2016|Hallegatte et al., 2016]] ; [[#Aklin--2018|Aklin et al., 2018]] ; [[#Simpson--2021c|Simpson et al., 2021c]] ). Integrating urban green infrastructure into adaptation planning in informal settlements can simultaneously unlock pathways for inclusivity and social justice ( [[#9.9.5|Section 9.9.5]] ; [[#Tozer--2020|Tozer et al., 2020]] ; [[#Wijesinghe--2021|Wijesinghe and Thorn, 2021]] ).

Social protection has been used for decades, particularly in eastern and southern Africa, to safeguard poor and vulnerable populations from poverty and food insecurity ( [[#Niño-Zarazúa--2012|Niño-Zarazúa et al., 2012]] ). Instruments of social protection include public works programmes, cash transfers, in-kind transfers, social insurance and microinsurance schemes that assist individuals and households to cope during times of crisis and minimise social inequality. Evidence from Ethiopia, Kenya and Uganda indicates national social protection programmes are effective in improving individual and household resilience to climate-related shocks, regardless of whether they aim specifically to address climate risks ( [[#Ulrichs--2019|Ulrichs et al., 2019]] ). Strengthening social protection and better integrating climate risk management into design of social protection programmes can help build long-term resilience to climate change ( [[#Hallegatte--2016|Hallegatte et al., 2016]] ; [[#Agrawal--2019|Agrawal et al., 2019]] ). For example, public works programmes can build climate resilience by targeting soil, water and ecosystem conservation and carbon sequestration, such as South Africa’s Working for Water Programme that restores river catchments to reduce fire risk and increase water supplies ( [[#Turpie--2008|Turpie et al., 2008]] ; [[#Norton--2020|Norton et al., 2020]] ).

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<span id="climate-insurance"></span>
==== 9.11.4.1 Climate Insurance ====

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African countries and communities are inadequately insured against climate risk. Insurance penetration is less than 2% of GDP ( [[#Swis%20Re--2019|Swis Re, 2019]] ) and 90% of natural catastrophe losses were uninsured in Africa in 2018 ( [[#Swis%20Re--2019|Swis Re, 2019]] ) leaving a large risk protection gap. The cost of reinsurance in Africa’s most mature insurance market—South Africa—has increased since 2017 due to climate-related payouts ( [[#SAIA--2018|SAIA, 2018]] ; [[#Simpson--2020|Simpson, 2020]] ), ''which is expected'' to further reduce the extent of insurance coverage. Emerging trends that seek to address this gap include innovative weather and drought index-based insurance schemes to transfer risk, forward-looking climate data and models to manage risk and insurers transitioning from risk transfer providers to proactive risk managers.

The most significant area of climate risk insurance innovation has occurred in weather and drought index-based insurance schemes that pay out fixed amounts based on the occurrence of an event instead of full indemnification against assessed losses (Table 9.12). However, despite the relatively low cost, uptake remains low due to affordability constraints, lack of awareness, access to and trust in products, distribution challenges, basis risk, poor transparency, challenges regarding the integration of complementary interventions (e.g., access to improved inputs or informal savings/credit) and poor perceptions/norms of insurance and risk transfer. Lack of data and models further hinders insurers’ ability to price risk correctly, which reduces value to clients ( [[#Greatrex--2015|Greatrex et al., 2015]] ; [[#Di%20Marcantonio--2017|Di Marcantonio and Kayitakire, 2017]] ; [[#WEF--2021|WEF, 2021]] ). Impact assessments point to potential but remain context-specific ( [[#Awondo--2019|Awondo, 2019]] ; [[#Hansen--2019b|Hansen et al., 2019b]] ; [[#Noritomo--2020|Noritomo and Takahashi, 2020]] ). In addition, there is no comprehensive overview of the number of people covered by such schemes, nor of the value they provide in terms of actual claims payouts. Lastly, donor and/or public funds still play an outsized role in launching and/or sustaining these schemes and schemes beyond weather and drought remain limited (Table 9.12).

'''Table 9.12 |''' Insurance opportunities to mitigate climate risk.

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

! '''Drought/'''

'''heatwave'''

! '''Flood'''

! '''Cyclone'''

! '''Fire'''

! '''Example'''

! '''Policyholders/ beneficiaries'''

! '''Reference'''

|-
| ''Index and parametric schemes— smallholder farmer''

| X

| X

|
| ACRE Africa, Pula, R4 Rural Resilience Initiative, KLIP, FISP, Ghana Agricultural Insurance Pool, Oko Crop Assurance

| Smallholder farmers

| [[#Greatrex--2015|Greatrex et al. (2015)]] ; [[#CTA--2019|CTA (2019)]] ; [[#Global%20Index%20Insurance%20Facility--2019|Global Index Insurance Facility (2019)]] ; [[#WFP--2020|WFP (2020)]] ; [[#Fava--2021|Fava et al. (2021)]] ; [[#OKO%20Finance--2021|OKO Finance (2021)]] ; [[#Pula--2021|Pula (2021)]] ; [[#Tsan--2021|Tsan et al. (2021)]]

|-
| ''Index and parametric schemes – sovereign and sub-sovereign''

| X

| X

| X

|
| African Risk Capacity

| Governments

| ARC (2019)

|-
| ''Index and parametric schemes – global''

| X

| X

|
| African and Asian Resilience in Disaster Insurance Scheme (ARDIS)

| Individuals and smallholder farmers

| Global Parametrics (2018)

|-
| ''Risk management and data collaboration''

| X

| X

| X

| X

| UNEP PSI

Santam

Tripartite Agreement

| Insurers and reinsurers, local municipalities, governments

| [[#Santam--2018|Santam (2018)]] ; [[#Forsyth--2019|Forsyth et al. (2019)]] ; [[#UNEP-FI--2019a|UNEP-FI (2019a)]] ; InsurResilience (2020); [[#Simpson--2020|Simpson (2020)]]

|-
| ''FinTech''

| X

| X

|
| X

| Lumkani, WorldCover, Econet, PlaNet Guarantee

| Individuals, smallholder farmers

| [[#Greatrex--2015|Greatrex et al. (2015)]] ; Hunter et al. (2018); [[#CTA--2019|CTA (2019)]] ; [[#UK%20Space%20Agency--2020|UK Space Agency (2020)]] ; [[#Tsan--2021|Tsan et al. (2021)]]

|}

Insurers and their clients are often unaware of their risk exposure, partly due to data and modelling gaps. Climate information services and related collaborations are increasingly helping to address this problem (see [[#9.4.5|Section 9.4.5]] ). Climate change attribution methods to estimate the contribution of human-casued climate change to the cost of parametric insurance offers possibilities for a sharing of the premium between the impacted African country and a global climate fund, such as the GCF ( [[#New--2020|New et al., 2020]] ). Technology companies and start-ups (including FinTechs) are also emerging as solutions to fill risk gaps, leveraging new approaches to data and technology through the use of sensors, drones and satellite imaging to speak to mainly agricultural risks, but also urban risks such as informal settlement fires, exacerbated by heat and drought (Table 9.12).

Ten African insurers formally committed to help manage climate risk on the continent through the Nairobi Declaration of the UNEP Principles for Sustainable Insurance (PSI) in 2021 ( [[#UNEP%20PSI--2021|UNEP PSI, 2021]] ). Some early examples of public–private partnerships with municipalities and governments to better manage climate risk are also emerging (Table 9.12).

<div id="9.11.5" class="h2-container"></div>

<span id="covid-19-recovery-stimulus-packages-for-climate-action"></span>
=== 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.

<div id="box-9.8" class="h2-container box-container"></div>

'''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]] ).

<div id="_idContainer112" class="Box_Header-continued"></div>

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

<div id="9.12" class="h1-container"></div>

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