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

=== 9.4.5 Climate Services, Perception and Literacy ===

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Policy actors across Africa perceive that human-caused climate change is already impacting their locales through a range of negative socioeconomic and environmental effects ( [[#Pasquini--2020|Pasquini, 2020]] ; Steynor and [[#Pasquini--2020|Pasquini, 2020]] ). They are highly concerned about and motivated to address these impacts ( [[#Hambira--2015|Hambira and Saarinen, 2015]] ; [[#Pasquini--2020|Pasquini, 2020]] ). Transformative responses to the impacts of climate change facilitate CRD and are informed by perceptions of climate variability and change and climate change literacy (Figure 9.11).

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'''Figure 9.11 |''' '''Climate services and climate literacy are important for informing transformative responses to climate change (including adaptation and mitigation responses)'''

'''(a)''' The importance of climate services and climate change literacy for more transformative responses to climate change in Africa adapted from Simpson et al. '''(2021a).''' Climate services promote climate resilient development by providing climate information for adaptation decision making ( [[#Street--2016|Street, 2016]] ; [[#Vaughan--2018|Vaughan et al., 2018]] ). Scalable uptake of climate services relies partly on climate risk perception of users, which is largely driven in Africa by experience and perception of local climate changes ( [[#Jacobs--2020|Jacobs and Street, 2020]] ; [[#Steynor--2020b|Steynor et al., 2020b]] ; Steynor and [[#Pasquini--2020|Pasquini, 2020]] ). Perception of climate change can occur without knowledge of its human-induced causes and its effects ( [[#Lee--2015|Lee et al., 2015]] ; [[#Alemayehu--2017|Alemayehu and Bewket, 2017]] ; [[#Andrews--2020|Andrews and Smirnov, 2020]] ). This can lead to coping responses to climate change which fall short of adaptation. Climate change literacy encompasses being aware of climate change and its anthropogenic causes and, together with climate services, can strengthen responses to climate change through better understanding of future risk ( [[#IPCC--2019b|IPCC, 2019b]] ; [[#Simpson--2021a|Simpson et al., 2021a]] ).

'''(b, c)''' Percentage of studies that have recorded that perception of temperature changes and precipitation changes agreed with local meteorological or climate records across 33 African countries (size of bubble indicates number of studies per country for both b and c. In b, agreement with temperature changes is indicated for all studies within a country in red, and articles indicating no agreement in orange; while in c, agreement with precipitation changes is indicated per country in dark blue and articles indicating no agreement in light blue. A total of 144 studies assessed across the 33 countries).

'''(d)''' Country-level rates of climate change literacy for 33 African countries (i.e., percentage of the population that have heard about climate change and think that human activity is wholly or partly the cause of climate change) [[#Simpson--2021a|Simpson et al. (2021a)]] .

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==== 9.4.5.1 Climate Information and Services ====

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Climate services (CS) broadly include the generation, tailoring and provision of climate information for use in decision making at all levels of society ( [[#Street--2016|Street, 2016]] ; [[#Vaughan--2018|Vaughan et al., 2018]] ). There is a range of climate service providers in Africa, including primarily National Meteorological and Hydrological Services (NMHS) and partner institutions, complemented by NGOs, the private sector and research institutions ( [[#Snow--2016|Snow et al., 2016]] ; [[#Harvey--2019|Harvey et al., 2019]] ), which offer the potential for public–private partnerships ( [[#Winrock--2018|Winrock, 2018]] ; [[#Harvey--2019|Harvey et al., 2019]] ).

International development funding has progressed the provision of CS and, together with technological advances and capacity-building initiatives, has increased the reliability of CS across Africa ( [[#Vogel--2019|Vogel et al., 2019]] ). Most CS investments have been towards the agricultural sector, with other focal sectors, including pastoralism, health, water, energy and disaster risk reduction, having only small CS initiatives directed towards them ( [[#Nkiaka--2019|Nkiaka et al., 2019]] ; [[#Carr--2020|Carr et al., 2020]] ). Despite this focus and investment, however, there remains a mismatch between the supply and uptake of CS in Africa as information is often inaccessible, unaffordable, not relevant to context or scale, and is poorly communicated ( [[#Singh--2018|Singh et al., 2018]] ; [[#Antwi-Agyei--2021|Antwi-Agyei et al., 2021]] ) (Table 9.4; Sections 9.4.1.5.1 and 9.13.4.1). Observational data required for effective regional CS, including trend analyses, seasonal climate assessment, modelling and model evaluation, is sparse and often of poor quality (Figure 9.11) and usually requires payment which renders it unaffordable ( [[#Winrock--2018|Winrock, 2018]] ).

A number of these challenges can be addressed through the transdisciplinary co-production of CS ( [[#Alexander--2019|Alexander and Dessai, 2019]] ; [[#Vogel--2019|Vogel et al., 2019]] ; [[#Carter--2020|Carter et al., 2020]] ). Co-production of CS involves climate information producers, practitioners and stakeholders, and other knowledge holders participating in equitable partnerships and dialogues to collaboratively identify climate-based risk and develop scale-relevant climate information to address this risk (Table 9.4) ( [[#Vincent--2018|Vincent et al., 2018]] ; [[#Carter--2020|Carter et al., 2020]] ).

'''Table 9.4 |''' Challenges and opportunities for Climate Services in Africa for the supply and uptake of climate services.

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

! '''Opportunities/solutions'''

! '''References'''

! '''Examples of programmes that address these challenges''' a

|-
| colspan="4"| '''Supply of Climate Services'''

|-
| Poor infrastructure (e.g., non-functioning observational networks; limited Internet bandwidth; lack of climate modelling capacity; issues of keeping pace with changing technology)

| * International funding for observation networks, data rescue and data sharing
* Regular NMHS budgets from governments
* Public–private partnerships

| [[#Snow--2016|Snow et al. (2016)]] ; [[#World%20Bank%20Group--2016|World Bank Group (2016)]] ; [[#Winrock--2018|Winrock (2018)]] ; [[#Cullmann--2020|Cullmann et al. (2020)]] ; [[#Meque--2021|Meque et al. (2021)]]

| ''East Africa and the West African Sahel (ENACTS programme)''

Working with NMHS to provide enhanced services by overcoming the challenges of data quality, availability and access.

Creating of reliable climate information suitable for national and local decision-making using station observations and satellite data to provide greater accuracy in smaller space and time scales.

|-
| Fragmented delivery of Climate Services

| * Greater collaboration between the NMHS and sector-specific specialists to create a central database of sector-based climate services

| [[#Winrock--2018|Winrock (2018)]] ; [[#Hansen--2019a|Hansen et al. (2019a)]]

| ''Rwanda (RCSA programme)''

Improving CS and agricultural risk management at local and national government levels in the face of a variable and changing climate.

|-
| Mismatch in time scales: short-term information more desirable (e.g., seasonal predictions as opposed to decadal or end of century projections)

| * Co-production of climate service products

| [[#Jones--2015|Jones et al. (2015)]] ; [[#Vincent--2018|Vincent et al. (2018)]] ; [[#Hansen--2019a|Hansen et al. (2019a)]] ; [[#Carr--2020|Carr et al. (2020)]] ; [[#Sultan--2020|Sultan et al. (2020)]]

| ''Burkina Faso (BRACED project)''

Strengthening technical and communication capacities of national meteorological services to enable partners to jointly develop forecasts tailored to support agro-pastoralists.

|-
| Development funding interventions operate on time scales that inhibit or restrict effective adaptation and neglect to build in considerations for sustainability post the funded intervention

| * Co-production of climate service products
* Endogenously driven climate services (services that are developed by regional actors, not by remote, usually developed nation actors)

| [[#Vincent--2018|Vincent et al. (2018)]] ; Vogel et al. (2019) [[#Vincent--2020a|Vincent et al. (2020a)]]

| ''Burkina Faso (BRACED project)''

Actors recognised the need to ensure continuation of CS post-project. Burkina Faso NMHS (ANAM) and National Council for Emergency Assistance and Rehabilitation (CONASUR) budgeted for the continued communication of CS and training of focal weather intermediaries. Local radio stations agreed to continue transmitting CS.

|-
| colspan="4"| '''Use of Climate Services'''

|-
| Insufficient access to usable data, including station data, and information suited to the decision context (including accessibility limitations based on gender and social inequalities)

| * Capacity development initiatives for Climate Services providers, intermediaries (including extension agents, NGO workers and others) and users
* User needs assessments
* Consistent monitoring and evaluation of Climate Services interventions

| [[#Jones--2015|Jones et al. (2015)]] ; [[#Winrock--2018|Winrock (2018)]] ; [[#Hansen--2019a|Hansen et al. (2019a)]] ; [[#Hansen--2019c|Hansen et al. (2019c)]] ; [[#Mercy%20Corps--2019|Mercy Corps (2019)]] ; [[#Nkiaka--2019|Nkiaka et al. (2019)]] ; [[#Carr--2020|Carr et al. (2020)]] ; [[#Cullmann--2020|Cullmann et al. (2020)]] ; [[#Gumucio--2020|Gumucio et al. (2020)]] ; [[#Sultan--2020|Sultan et al. (2020)]]

Figure 9.11

| ''Kenya, Ethiopia, Ghana, Niger and Malawi (ALP Programme)''

Co-production of relevant information for decision making and planning at seasonal time scales. The methods and media for communication and messages differ between different users. Strong emphasis on participation by women.

|-
| Limited capacity of users to understand or request appropriate Climate Services products

| * Co-production of climate service products
* Capacity development

| [[#Snow--2016|Snow et al. (2016)]] ; [[#Singh--2018|Singh et al. (2018)]] ; [[#Vincent--2018|Vincent et al. (2018)]] ; [[#Nkiaka--2019|Nkiaka et al. (2019)]] ; [[#Daniels--2020|Daniels et al. (2020)]]

| ''Cities in Zambia, Namibia, Mozambique, Zimbabwe, Botswana, Malawi and South Africa (FRACTAL programme)''

Repeated interactions between each represented sector to learn and more completely understand the different contexts of each represented party and build understanding through an ethic of collaboration for solving climate-related problems in each unique city.

|-
| Lack of user trust in the information

| * Co-production of climate service products
* Combine scientific and Indigenous forecasts
* Demonstrate added value of the climate service

| [[#Vincent--2018|Vincent et al. (2018)]] ; [[#Nkiaka--2019|Nkiaka et al. (2019)]] ; [[#Vaughan--2019|Vaughan et al. (2019)]] ; Vogel et al. (2019); [[#Nyadzi--2021|Nyadzi et al. (2021)]]

| ''Tanzania (ENACTS programme)''

Co-production to inform malaria decisions systematically and change relationships, trust, and demand in a manner that had not been realised through previous singular and siloed approaches.

|-
| Socioeconomic, and institutional barriers (limited professional mandates, financing limitations, institutional cooperation)

| * Regular NMHS budgets from governments
* Public–private partnerships
* Supportive institutions, policy frameworks and individual capacity and agency

| [[#Snow--2016|Snow et al. (2016)]] ; [[#World%20Bank%20Group--2016|World Bank Group (2016)]] ; [[#Winrock--2018|Winrock (2018)]] ; [[#Harvey--2019|Harvey et al. (2019)]] ; [[#Vincent--2020b|Vincent et al. (2020b)]]

|
|}

Notes:

(a) Reproduced from [[#Carter--2020|Carter et al. (2020)]] with permission.

However, the effectiveness of co-production processes are hindered by aspects such as inequitable power relationships between different types of knowledge holders (e.g., scientists and practitioners), inequitable distribution of funding between developed country and African partners that favours developed country partners, an inability to develop sustained trust relationships as a result of short-funding cycles, a lack of flexibility due to product-focused engagements and the scalability of co-production to enable widespread reach across Africa as the process is usually context specific ( ''high confidence'' ) ( [[#Vincent--2018|Vincent et al., 2018]] ; [[#Vogel--2019|Vogel et al., 2019]] ; 2020a).

Despite these challenges, the inclusive nature of co-production has had a positive influence on the uptake of CS into decision making where it has been applied (Table 9.4; Figure 9.12; [[#Vincent--2018|Vincent et al., 2018]] ; [[#Vogel--2019|Vogel et al., 2019]] ; [[#Carter--2020|Carter et al., 2020]] ; [[#Chiputwa--2020|Chiputwa et al., 2020]] ) ( ''medium confidence'' ), through sustained inter/transdisciplinary relationships and capacity development ( [[#Norström--2020|Norström et al., 2020]] ), strategic financial investment, fostering of ownership of resulting products and the combining of scientific and other knowledge systems ( [[#Carter--2020|Carter et al., 2020]] ; [[#Steynor--2020a|Steynor et al., 2020a]] ). There is ''high confidence'' that together with improved institutional capacity building and strategic financial investment, CS can help African stakeholders adapt to projected climate risks (Figure 9.11).

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[[File:758f3feab3aac0a315c4b408a80ef5fa IPCC_AR6_WGII_Figure_9_012.png]]

'''Figure 9.12 |''' '''The inclusive nature of co-production has had a positive influence on the uptake of climate services into decision making in Africa.''' Selected examples of the co-production of climate services and the sectors involved. Icons indicate sectors and numbers show the programmes under which the co-production engagements occurred. Programmes listed are (1) AMMA-2050: Combining Scenario Games, Participatory Modelling and Theatre Forums to Co-produce Climate Information for Medium-term Planning, (2,3) BRACED: Sharing Lessons on Promoting Gender Equality through a ‘Writeshop’, (4) RCSA: Bringing Climate Services to People Living in Rwanda’s Rural Areas, (5) ALP: Participatory Scenario Planning for Local Seasonal Climate Forecasts and Advisories, (6) Climate Risk Narratives: Co-producing Stories of the Future, (7) ENACTS: Developing Climate Services for Malaria Surveillance and Control in Tanzania, (8) FATHUM: Forecast for Anticipatory Humanitarian Action, (9) FRACTAL: Learning Labs, Dialogues and Embedded Researchers in Southern African Cities, (10) FONERWA: Climate Risk Screening Tool, (11) MHEWS: Multi-hazard Early Warning System for Coastal Tanzania, (12) Resilient Transport Strategic Assessment for Dar es Salaam, (13) RRA: Climate Attribution for Extreme Weather Events in Ethiopia and Kenya, (14) UMFULA: Co-producing Climate Information for Medium-term Planning in the Water-Energy-Food Nexus, (15) IRRP: Building Resilience in Tanzania’s Energy Sector Planning, (16) PRISE: Co-exploring Relevant Evidence for Policy Change in Kenya, (17) NMA ENACTS: An Example of a Co-produced Climate Service Fit for Purpose, (18) REACH: Improving Water Security for the Poor in Turkana County, Kenya, (19) DARAJA: Co-designing Weather and Climate Information Services for and with Urban Informal Settlements in Nairobi and Dar es Salaam, (20) ForPAc: Co-producing Approaches to Forecast-based Early Action for Drought and Floods in Kenya, (21) HIGHWAY: Co-produced Impact-based Early Warnings and Forecasts to Support Fishing Communities on Lake Victoria, (22) HyCRISTAL: Using Video to Initiate Farmer Dialogue with Local Government in Mukono, Uganda, (23) SCIPEA: Co-produced Seasonal Forecasts for More Effective Management of Hydropower Supply in Kenya, (24) Weather Wise: Co-producing Weather and Climate Radio Content for Farmers, Fishermen and Pastoralists in East Africa. See [[#Carter--2020|Carter et al. (2020)]] for details and outcomes of each engagement. Source: [[#Carter--2020|Carter et al. (2020)]] .

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==== 9.4.5.2 Community Perceptions of Climate Variability and Change ====

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Perceptions of climate variability and change affect whether and how individuals and institutions act, and thus contribute to the success or failure of adaptation policies related to weather and climate ( [[#Silvestri--2012|Silvestri et al., 2012]] ; [[#Arbuckle--2015|Arbuckle et al., 2015]] ; [[#Simpson--2021a|Simpson et al., 2021a]] ).

A recent Afrobarometer study covering 34 African countries found 67% of Africans perceive climate conditions for agricultural production to have worsened over time, and report drought as the main extreme weather event to have worsened in the past decade ( [[#Selormey--2019|Selormey et al., 2019]] ). Of these participants, across all socioeconomic strata, 71% of those who were aware of the concept of climate change agreed that it needs to be stopped, but only 51% expressed confidence about their ability to make a difference. East Africans (63%) were almost twice as likely as north Africans (35%) to report that the weather for growing crops had worsened. Additionally, people engaged in occupations related to agriculture (farming, fishing or forestry) were more likely to report negative weather effects (59%) than those with other livelihoods (45%) ( [[#Selormey--2019|Selormey et al., 2019]] ). Similar perceptions have been reported among a diversity of rural communities in many sub-Saharan African countries ( [[#Mahl--2020|Mahl et al., 2020]] ; [[#Simpson--2021a|Simpson et al., 2021a]] ).

Rural communities, particularly farmers, are the most studied groups for climate change perception. They perceive the climate to be changing, most often reporting changes in rainfall variability, increased dry spells, decreases in rainfall and increased temperatures or temperature extremes. They perceive these changes to bring a range of negative socioeconomic and environmental effects ( [[#Alemayehu--2017|Alemayehu and Bewket, 2017]] ; [[#Liverpool-Tasie--2020|Liverpool-Tasie et al., 2020]] ; [[#Simpson--2021a|Simpson et al., 2021a]] ). In some cases, farmers’ perceptions of changes in weather and climate frequently match climate records for decreased precipitation totals, increased drought frequency, shorter rainy season and rainy season delay, and increased temperatures (Figure 9.11; [[#Rurinda--2014|Rurinda et al., 2014]] ; [[#Boansi--2017|Boansi et al., 2017]] ; [[#Ayanlade--2018|Ayanlade et al., 2018]] ), but not in all cases or not for all perceived changes, with common discrepancies in perceived lower rainfall totals ( [[#Alemayehu--2017|Alemayehu and Bewket, 2017]] ; [[#Ayal--2017|Ayal and Leal Filho, 2017]] ; [[#Simpson--2021a|Simpson et al., 2021a]] ).

Farming experience, access to extension services and increasing age are the most frequently cited factors positively influencing the perceptions of climate changes ( [[#Alemayehu--2017|Alemayehu and Bewket, 2017]] ; [[#Oduniyi--2019|Oduniyi and Tekana, 2019]] ). Personal experience of climate-related changes and their impacts appears to be an important factor influencing perceptions through shaping negative associations, for example, experience of flash floods ( [[#Elshirbiny--2020|Elshirbiny and Abrahamse, 2020]] ) or direct effect on economic activity, indicating that perception is not restricted to crop farmers ( [[#Liverpool-Tasie--2020|Liverpool-Tasie et al., 2020]] ). However, perceptions show common misconceptions about the causes of climate change, which has implications for climate action ( [[#Elshirbiny--2020|Elshirbiny and Abrahamse, 2020]] ), highlighting the importance of climate change literacy.

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==== 9.4.5.3 Climate Change Literacy ====

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Understanding the human cause of climate change is a strong predictor of climate change risk perception ( [[#Lee--2015|Lee et al., 2015]] ) and a critical knowledge foundation that can affect the difference between coping responses and more informed and transformative adaptation (Figure 9.11; [[#Oladipo--2015|Oladipo, 2015]] ; [[#Mutandwa--2019|Mutandwa et al., 2019]] ). At a minimum, climate change literacy includes both having heard of climate change and understanding it is, at least in part, caused by people ( [[#Simpson--2021a|Simpson et al., 2021a]] ). However, large inequalities in climate change literacy exist between and within countries and communities across Africa.

The average national climate change literacy rate in Africa is only 39% (country rates range from 23–66%) (Figure 9.11). Of 394 sub-national regions surveyed by Afrobarometer, 8% (37 regions in 16 countries) have a climate change literacy rate lower than 20%, while only 2% (8 regions) score higher than 80%, which is common across European countries ( [[#Simpson--2021a|Simpson et al., 2021a]] ). Striking differences exist when comparing sub-national units within countries. Climate change literacy rates in Nigeria range from 71% in Kwara to 5% in Kano, and within Botswana from 69% in Lobatse to only 6% in Kweneng West ( [[#Simpson--2021a|Simpson et al., 2021a]] ). Education is the strongest positive predictor of climate change literacy, particularly tertiary education, but poverty decreases climate change literacy and climate change literacy rates average 12.8% lower for women than men ( [[#Simpson--2021a|Simpson et al., 2021a]] ).

As the identified factors driving climate change literacy overlap with broader developmental challenges on the continent, policies targeting these factors (e.g., increased education) can potentially yield co-benefits for both climate change adaptation as well as progress towards SDGs, particularly education and gender equality ( [[#Simpson--2021a|Simpson et al., 2021a]] ). Progress towards greater climate change literacy affords a concrete opportunity to mainstream climate change within core national and sub-national developmental agendas in Africa towards more CRD pathways. Synergies with CS can also overcome gendered deficits, for example, although women are generally less climate change aware and more vulnerable to climate change than men in Africa, they are generally more likely to adopt climate-resilient crops when they are climate change aware and have exposure to extension services ( [[#Acevedo--2020|Acevedo et al., 2020]] ; [[#Simpson--2021a|Simpson et al., 2021a]] ).

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'''Box 9.1 | Vulnerability Synthesis'''
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Vulnerability in Africa is socially, culturally and geographically differentiated among climatic regions, countries and local communities, with climate change impacting the health, livelihoods and food security of different groups to different extents ( [[#Gan--2016|Gan et al., 2016]] ; [[#Onyango--2016a|Onyango et al., 2016a]] ; [[#Gumucio--2020|Gumucio et al., 2020]] ). This synthesis emphasises intersectionality within vulnerable groups as well as their position within dynamic social and cultural contexts ( [[#Wisner--2016|Wisner, 2016]] ; [[#Kuran--2020|Kuran et al., 2020]] ), and highlights the differential impacts of climate change and restricted adaptation options available to vulnerable groups across African countries (see also Cross-Chapter Box GENDER in Chapter 18).

Vulnerability and exposure to the impacts of climate change are complex and affected by multiple, interacting non-climatic processes, which together influence risk, including socioeconomic processes ( [[#Lwasa--2018|Lwasa et al., 2018]] ; [[#UNCTAD--2020|UNCTAD, 2020]] ), resource access and livelihood changes ( [[#Jayne--2019b|Jayne et al., 2019b]] ) and intersectional vulnerability among social groups (Figure Box 9.1.1; [[#Rao--2020|Rao et al., 2020]] ). Socioeconomic processes encompass broader social, economic and governance trends, such as expanded investment in large energy and transportation infrastructure projects ( [[#Adeniran--2020|Adeniran and Daniell, 2020]] ), rising external debt ( [[#Edo--2020|Edo et al., 2020]] ), changing role of the state in social development ( [[#Wunsch--2014|Wunsch, 2014]] ), environmental management ( [[#Ramutsindela--2019|Ramutsindela and Büscher, 2019]] ) and conflict, as well as those emanating from climate change mitigation and adaptation projects ( [[#Beymer-Farris--2012|Beymer-Farris and Bassett, 2012]] ; [[#van%20Baalen--2018|van Baalen and Mobjörk, 2018]] ; [[#Simpson--2021b|Simpson et al., 2021b]] ). These macro trends shape both urban and rural livelihoods, including the growing diversification of rural livelihoods through engagement in the informal sector and other non-farm activities, and are mediated by complex and intersecting factors like gender, ethnicity, class, age, disability and other dimensions of social status that influence access to resources ( [[#Luo--2019|Luo et al., 2019]] ). Research increasingly highlights the intersectionality of multiple dimensions of social identity and status that are associated with greater susceptibility to loss and damage ( [[#Caparoci%20Nogueira--2018|Caparoci Nogueira et al., 2018]] ; [[#Li--2018|Li et al., 2018]] ).

Arid and semi-arid countries in the Sahelian belt and the greater Horn of Africa are often identified as the most vulnerable regions on the continent ( [[#Closset--2017|Closset et al., 2017]] ; [[#Serdeczny--2017|Serdeczny et al., 2017]] ). Particularly vulnerable groups include pastoralists ( [[#Wangui--2018|Wangui, 2018]] ; [[#Ayanlade--2019|Ayanlade and Ojebisi, 2019]] ), fishing communities ( [[#Belhabib--2016|Belhabib et al., 2016]] ; [[#Muringai--2019a|Muringai et al., 2019a]] ), small-scale farmers ( [[#Ayanlade--2017|Ayanlade et al., 2017]] ; [[#Mogomotsi--2020|Mogomotsi et al., 2020]] ; see [[#9.8.1|Section 9.8.1]] ) and residents of formal and informal urban settlements (see [[#9.9|Section 9.9]] ). Research has identified key macro drivers, as well as multiple dimensions of social status that mediate differential vulnerability in different African contexts. For example, the contemporary vulnerability of small-scale rural producers in semi-arid northern Ghana has been shaped by colonial economic transformations ( [[#Ahmed--2016|Ahmed et al., 2016]] ), more recent neoliberal reforms reducing state support ( [[#Fieldman--2011|Fieldman, 2011]] ) and the disruption of local food systems due to increasing grain imports ( [[#Nyantakyi-Frimpong--2015|Nyantakyi-Frimpong and Bezner-Kerr, 2015]] ). Age interacts with other dimensions of social status, shaping differential vulnerability in several ways. Projected increases in mean temperatures and longer and more intense heat waves (Figure Box 9.1.1) may increase health risks for children and elderly populations by increasing risks associated with heat stress ( [[#Bangira--2015|Bangira et al., 2015]] ; [[#Cairncross--2018|Cairncross et al., 2018]] ). Temperature extremes are associated with increased risk of mortality in Ghana, Burkina Faso, Kenya and South Africa, with greatest increases among children and the elderly ( [[#Bangira--2015|Bangira et al., 2015]] ; [[#Amegah--2016|Amegah et al., 2016]] ; [[#Omonijo--2017|Omonijo, 2017]] ; [[#Wiru--2019|Wiru et al., 2019]] ; see [[#9.10.2.3.1|Section 9.10.2.3.1]] ).

Rural African women are often disadvantaged by traditional, patriarchal decision-making processes and lack of access to land—issues compounded by kinship systems (that, is matrilineal or patrilineal), migrant status, age, type of household, livelihood orientation and disability in determining their adaptive options ( [[#Ahmed--2016|Ahmed et al., 2016]] ; see [[#9.8.1|Section 9.8.1]] ; 9.11.1.2; Box 9.8). Differential agricultural productivity between men and women is about 20–30% or more in dryland regions of Ethiopia and Nigeria ( [[#Ghanem--2011|Ghanem, 2011]] ) and challenges women’s ability to adapt to climate change. Limited access to agricultural resources and limited benefits from agricultural policies, compounded by other social and cultural factors, make women more vulnerable to climatic risks ( [[#Shukla--2021|Shukla et al., 2021]] ). Kinship systems can contribute to their vulnerability and capacity to adapt. Women in matrilineal systems have greater bargaining power and have access to more resources than those in patrilineal systems ( [[#Chigbu--2019|Chigbu, 2019]] ; [[#Robinson--2021|Robinson and Gottlieb, 2021]] ; See section 9.8.1; 9.11.1.2).

[[File:4ddb413fc96ddcab0e88da98e9bff167 IPCC_AR6_WGII_Figure_9_Box_9_1_1.png]]

'''Figure Box 9.1.1 |''' '''Factors contributing to the progression of vulnerability to climate change in African contexts considering socioeconomic processes, resource access, livelihood changes, and intersectional vulnerability among social groups.''' This figure reflects a synthesis of vulnerability across sections of this chapter and highlights how the interactions of multiple dimensions of vulnerability compound each other to increase overall vulnerability ( [[#Potts--2008|Potts, 2008]] ; [[#Nielsen--2010|Nielsen and Reenberg, 2010]] ; [[#Akresh--2011|Akresh et al., 2011]] ; [[#Eriksen--2011|Eriksen et al., 2011]] ; [[#Beymer-Farris--2012|Beymer-Farris and Bassett, 2012]] ; Davis et al., 2012; [[#Adom--2014|Adom, 2014]] ; [[#Akello--2014|Akello, 2014]] ; [[#Headey--2014|Headey and Jayne, 2014]] ; [[#Otzelberger--2014|Otzelberger, 2014]] ; [[#Wunsch--2014|Wunsch, 2014]] ; [[#Conteh--2015|Conteh, 2015]] ; [[#Huntjens--2015|Huntjens and Nachbar, 2015]] ; [[#Spencer--2015|Spencer, 2015]] ; [[#Adetula--2016|Adetula et al., 2016]] ; [[#Djoudi--2016|Djoudi et al., 2016]] ; [[#Kuper--2016|Kuper et al., 2016]] ; [[#Stark--2016|Stark and Landis, 2016]] ; [[#Allard--2017|Allard, 2017]] ; [[#Anderson--2017|Anderson, 2017]] ; [[#Asfaw--2017|Asfaw et al., 2017]] ; [[#Hufe--2017|Hufe and Heuermann, 2017]] ; [[#Hulme--2017|Hulme, 2017]] ; Paul and wa G ĩ th ĩ nji, 2017; [[#Rao--2017|Rao et al., 2017]] ; [[#Serdeczny--2017|Serdeczny et al., 2017]] ; [[#Tesfamariam--2017|Tesfamariam and Zinyengere, 2017]] ; [[#Tierney--2017|Tierney et al., 2017]] ; [[#Waha--2017|Waha et al., 2017]] ; [[#Chihambakwe--2018|Chihambakwe et al., 2018]] ; [[#Cholo--2018|Cholo et al., 2018]] ; [[#Jenkins--2018|Jenkins et al., 2018]] ; [[#Keahey--2018|Keahey, 2018]] ; [[#Lwasa--2018|Lwasa et al., 2018]] ; [[#Makara--2018|Makara, 2018]] ; [[#Nyasimi--2018|Nyasimi et al., 2018]] ; [[#Petesch--2018|Petesch et al., 2018]] ; [[#Schuman--2018|Schuman et al., 2018]] ; [[#Theis--2018|Theis et al., 2018]] ; [[#van%20Baalen--2018|van Baalen and Mobjörk, 2018]] ; [[#van%20der%20Zwaan--2018|van der Zwaan et al., 2018]] ; [[#Adepoju--2019|Adepoju, 2019]] ; [[#Adzawla--2019b|Adzawla et al., 2019b]] ; [[#Bryceson--2019|Bryceson, 2019]] ; [[#Grasham--2019|Grasham et al., 2019]] ; [[#Jayne--2019a|Jayne et al., 2019a]] ; [[#Lowe--2019|Lowe et al., 2019]] ; [[#Lunga--2019|Lunga et al., 2019]] ; [[#OGAR--2019|OGAR and Bassey, 2019]] ; [[#Onwutuebe--2019|Onwutuebe, 2019]] ; [[#Ramutsindela--2019|Ramutsindela and Büscher, 2019]] ; [[#Sulieman--2019|Sulieman and Young, 2019]] ; [[#Torabi--2019|Torabi and Noori, 2019]] ; [[#Adeniran--2020|Adeniran and Daniell, 2020]] ; [[#Alexander--2020|Alexander, 2020]] ; [[#Clay--2020|Clay and Zimmerer, 2020]] ; [[#Devonald--2020|Devonald et al., 2020]] ; [[#Dolislager--2020|Dolislager et al., 2020]] ; [[#Edo--2020|Edo et al., 2020]] ; [[#Kaczan--2020|Kaczan and Orgill-Meyer, 2020]] ; [[#Lammers--2020|Lammers et al., 2020]] ; [[#World%20Bank--2020b|World Bank, 2020b]] ; [[#Asiama--2021|Asiama et al., 2021]] ; [[#Azong--2021|Azong and Kelso, 2021]] ; [[#Birgen--2021|Birgen, 2021]] ; [[#Paalo--2021|Paalo and Issifu, 2021]] ; [[#Simpson--2021b|Simpson et al., 2021b]] ).

Knowledge gaps on Vulnerability in Africa and Uneven Acces to Resources

The differential impacts of climate change and adaptation options available to vulnerable groups in Africa are a critical knowledge gap. More research is needed to examine the intersection of different dimensions of social status on climate change vulnerability in Africa ( [[#Thompson-Hall--2016|Thompson-Hall et al., 2016]] ; [[#Oluwatimilehin--2021|Oluwatimilehin and Ayanlade, 2021]] ). More analysis of vulnerability based on gender and other social and cultural factors is needed to fully understand the impacts of climate change, the interaction of divergent adaptive strategies, as well as the development of targeted adaptation and mitigation strategies, for example, for women in patrilineal kinship systems, people living with disabilities, youth, girls and the elderly. Finally, there is an urgent need to build capacity among those conducting vulnerability assessments, so that they are familiar with this intersectionality lens.

Additional information and capacity development through education and early warning systems could enhance vulnerable groups’ ability to cope and adapt their livelihoods ( [[#Jaka--2018|Jaka and Shava, 2018]] ). However, some groups of people may struggle to translate information into actual changes ( [[#Makate--2019|Makate et al., 2019]] ; [[#McOmber--2019|McOmber et al., 2019]] ). Lack of access to assets and social networks, for example, among older populations, are critical limitations to locally driven or autonomous adaptation and limit potential benefits from planned adaptation actions (e.g., adoption of agricultural technologies or effective use of early warning systems).

There is an urgent need for societal and political change to realise potential benefits for these vulnerable groups in the long term ( [[#Nyasimi--2018|Nyasimi et al., 2018]] ). There is a need for gender-sensitive climate change policies in many African countries and gender-responsive policies, implementation plans and budgets for all local-level initiatives ( [[#Wrigley-Asante--2019|Wrigley-Asante et al., 2019]] ).

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