Map 1: Changes in days with high stream flow
Source (Schulze 2011)
Key factors in determining the sensitivity of particular areas to flooding include slope, soil types, and land
cover, as well as the presence of bulk water infrastructure such as dams capable of accumulating stream
flow. Human settlements and infrastructure tend to significantly increase the runoff rate due to reductions in
vegetative land cover and degrading of wetland.
1.1.1.
Veld fires
Wildfires (termed veldfires in South Africa, and known as bushfires or wild-land fires elsewhere) are a natural
phenomenon in many of South Africa’s ecosystems, but they cause damage and death in areas of human
settlements, and are particularly damaging to the plantation forest industry. During the 2008 – 2009 period,
wildfires caused damage estimated at R1,750 million, and 34 deaths have resulted (DEA 2011).
As shown in Error! Reference source not found. and Error! Reference source not found., climate
change is expected to result in an increased number of consecutive dry days and an increase in annual
average temperatures. These climate changes, coupled with land use changes such as afforestation due to
commercial plantations and the presence of invasive alien species, are associated with an increased in the
incidence and destructive impact of veld fires. Map 18 shows an assessment of the current spatial
distribution of veld and forest fire risk across the country.
Map 2: Veld and Forest Fire Risk
Source: Department of Agriculture, Forestry and Fisheries, 2009
2. Adaptive Capacity
Adaptive capacity refers to the ability of people to make the required changes that will enable them to survive
a changing climate. Adaptive capacity is therefore defined bythe resources, infrastructure and services
available to people to respond to the risks of climate change. Further, adaptive capacity is defined by how
people will experience hazard exposure; therefore it reflects the multiple stressors which people experience
such as poverty, ill-health or unemployment. Adaptive capacity is inversely related tosocial vulnerability – a
community with high adaptive capacity will have low levels of social vulnerability to climate change and vice
versa.
In assessing the adaptive capacity of rural human settlements, certain key indicators of social vulnerability
for rural settlements were spatially analysed. These vulnerabilities were grouped under three headings and
weighted in order to arrive at a composite spatial mapping displaying social vulnerability/adaptive capacity
across the country. The indicators were chosen to focus on the following aspects of adaptive capacity to
climate change:

Infrastructure and services: Access to services and dwelling types (30% of total).

Health: Malnutrition and primary health care (20% of total).

Socio-economic vulnerability: Land ownership, household income, gender and age profile (50% of
total).
For the purposes of simplicity in spatially mapping social vulnerability, in many of the maps that follow high
scores represent a high level of socially vulnerability.
2.1.
Infrastructure and services
2.1.1.
Access to basic services
This indicator uses access to basic water, electricity and sanitation services as a proxy for infrastructure in
the GIS model. Many people remain marginalized from access to services such as piped water, electricity,
basic sanitation, housing and roads. Communities with good infrastructure are more resilient to climate
change particularly in terms of health and in terms of being less directly dependent on natural resources for
the fulfilment of their basic needs. Municipal wards with low levels of access to basic services compared to
the national average score higher on this indicator.
Many rural households in South Africa are not connected to the electricity grid and therefore rely on other
sources of energy such as wood, gas, dung, or paraffin. A reliable source of energy within dwellings is an
important component of well-being. Access to energy within the household for lighting, cooking and heating
is especially beneficial for women as they are most often charged with sourcing energy; which is unpaid
work, and expends energy that could be devoted to other activities such as food production. Moreover,
households which are reliant on wood as a primary energy source will contribute to the land degradation.
Another basic service which many communities across South Africa are marginalized from is refuse removal.
The removal of refuse refers to the removal of solid waste from close proximity of human dwellings and is
mandated to local municipalities. Not only is the dumping of solid waste a climate change hazard because of
the release of methane; but the build-up of waste near human settlements also presents serious threats to
human health and presents a substantial fire risk.
Many households in South Africa are further marginalized from the access to a close supply of piped water.
Not only does this result in household activities being centred on the collection of water (which is often a
strenuous and timely task); but also means that households may rely on unsustainable and unclean water
resources. The use of limited natural water resources could contribute to future water scarcity and unclean
water can have serious adverse effects for human health.
Yet another basic service that is not adequately delivered to many communities in South Africa and
particularly to rural communities is sanitation. Improved sanitation is vital in protecting the health of
communities and safeguarding communities from infectious diseases of which the incidence will increase as
the climate continues to change in South Africa.
The combined score for access to basic services by municipal ward is shown in the map below.
Map 3: Combined score for access to basic services
Source: Calculated from Statistics SA Census 2011 data
2.1.2.
Type of dwelling
Type of dwelling refers to the type of structure in which people live. Climate change will present a number of
risks and hazards, such as an increase in temperature and an increase in the risk of flooding. It is imperative
that people’s shelters are able to endure these challenges. Buildings that are deemed resilient may have
some of the following features: flood and lightning protection, efficient water systems for drought protection,
cool spaces, heat reflective surfaces or damp proofing. Furthermore houses should be strategically placed to
avoid flood plains and green spaces. Informal settlements, on the other hand, are generally developed on
marginalized land and constructed with second rate building materials offer little protection against the
associated environmental risks of climate change. Not only are these dwelling types vulnerable to
environmental hazards but they also present significant health risks for their inhabitants such as a lack of air
circulation inside the dwelling. This assessment looked for areas with large proportions of informal dwellings
as an indicator of a lower adaptive capacity.
Map 4: Municipal wards with a large proportion of informal dwellings
Source: Calculated from Statistics SA Census 2011 data
2.2.
2.2.1.
Health
Population age profile
The age profile refers to the average ages of the population of South Africa. Census data shows that 28.4%
of the population of South Africa is between the ages of 0 and 14 years and 21% of the population is
between the ages of 15 and 24 years; rendering the population of South Africa extremely young. The median
age of the South African population is 25 years (Indexmundi 2013).
Many rural areas are populated by high numbers of children and the elderly as people of a working age often
migrate to urban areas in search of work. Children and the elderly are more vulnerable to climate change.
The reasons behind this vulnerability are that children and the elderly are more susceptible to illness and the
incidence and severity of disease is expected to increase. Children and the elderly are particularly vulnerable
to severe heat stress, food insecurity and malnutrition all of which can catalyse the opportunity for other
types of illnesses.
In this assessment, scores were assigned to municipal wards with high numbers of children and older
people, or low numbers of young people compared to the national average and using the following classes:
0-14: child, 15-39: young, 40+: older. These scores are shown in the map below.
Map 5: Municipal wards with unfavourable age profiles
Source: Calculated from Statistics South Africa Census 2011 data
2.2.2.
Primary health care utilisation rate
The primary health care (PHC) utilisation rate indicator measures the average number of PHC visits per
person per year to a public PHC facility. The indicator is calculated by dividing the total PHC annual
headcount by the total catchment population. South Africa is currently aiming for a target of 3.5 PHC visits
per person per year. The Primary Health Care Utilization rate provides an indication of areas in which people
have or do not have regular and easy access to medical care from medical facilities and therefore serves as
an indication of the amount and location of people that are more vulnerable to climate change because of
inadequate health services. In rural areas, medical facilities are often far apart ill-equipped. Since climate
change is likely bring new environmental stressors upon the health of communities such as heat stress,
waterborne diseases and increased malnutrition, access to regular and quality medical care will enhances
the adaptive capacity of rural settlements.
A score from 1 to 5 will be assigned to values in bands across the total range of values. A score of 5 will
represent a low adaptive capacity while a score of 1 will represent a high adaptive capacity.
Map 6: Primary Health Care Utilization Rate
Source: The District Health Barometer 2010/11 by the Health Systems Trust of South Africa. Data obtained
from Statistics South Africa; 2010
2.2.3.
Severe malnutrition in children under 5 years
This indicator measures the number of new cases of children who weigh below 60% of their expected
weight-for-age per 1000 children in the target population which is children under the age of five. Areas
where malnutrition is high already experience food insecurity due to a variety of reasons such as poverty and
the incapacity to grow enough of the right kinds of food. These incapacities largely affect rural communities
who experience erratic income flows and are reliant on subsistence farming. Climate change will increase
the already existing barriers to food security.
A score of 5 will represent a low adaptive capacity while a score of 1 will represent a high adaptive capacity.
Map 7: Severe Malnutrition in children under the age of 5 years
Source: The District Health Barometer 2010/11 by the Health Systems Trust of South Africa. Data obtained
from Statistics South Africa; 2010.
2.3.
2.3.1.
Economic vulnerability
Income
The annual household income refers to the total amount of money that each household in South Africa earns
per year. This income can be derived from formal or informal employment and constitutes any activity which
a member of the household undertakes that is rewarded in monetary terms. Income and expenditure levels
remain significantly varied across population groups in South Africa with black households earning the least
(StatsSA 2012). Households with a higher income are less susceptible to poverty and the multiple stressors
that contribute to climate change vulnerability. Although poverty is not synonymous with climate change
vulnerability, it is a major driver of climate vulnerability. It is assumed here that households with higher
annual incomes have a higher adaptive capacity than households with lower annual incomes. In this
assessment scores were assigned to wards with high percentages of poor households and low percentages
of high-income households compared to the national average. The following classes were used: low: 0 R38 200, medium: R38 201 - R153 800, high: >R 153 800. 22 shows the resultant income scores by
municipal ward – low scores indicate fewer low-income households and more high-income households.
Map 8: Household income scores
Source: Calculated from Statistics SA Census 2011 data
2.3.2.
Employment
Employment status of the head of the household refers to whether the head of the household is employed,
unemployed, a discouraged work seeker, in another form economically inactive or under the age of 15 years
of age (in other words a child headed household). In South Africa the household is generally understood to
be a dwelling with people who eat together and share resources and who stay in that dwelling for the
majority of the time. The general understanding of the household head is the person who either owns or is in
control of the property on which the dwelling sits, is the primary income provider within the household unit
and has the decision making power on income, resource use and distribution (StatsSA 2013). The
employment status of the head of the household is a valuable contributing factor towards adaptive capacity
since the difference between employment and non-employment means a substantial difference in the
availability of income and resources for poverty alleviation.
In this study, scores were assigned to wards with low percentages of employed people or high percentages
of discouraged work-seekers, economically in active household heads, or child-headed households. The
map below shows the resultant scores used in the model for each municipal ward.
Map 9:Scores used for employment status of the household head
Source: Calculated from Statistics SA Census 2011 data
2.3.3.
Gender
Gender inequalities exist in South Africa and these inequalities are often more pronounced in the rural areas.
In South Africa women already experience multiple stressors; such as unpaid and lowly paid work, child
rearing, and insufficient access to basic services. Further it is challenging for women to gain tenure of land
under the governance of traditional leadership. Climate change is expected to make already existing
development challenges worse; specifically for female headed households. Municipal wards with the
percentage female-headed households greater than two thirds of the national average scored on this
indicator.
Map 10: Municipal wards where the proportion of female-headed households is greater than two
thirds of the national average
Source: Calculated from Statistics SA Census 2011 data
2.3.4.
Land Tenure status
Land Tenure refers to the conditions under which land or buildings are being occupied. The linkages
between climate change and land tenure are complicated but it is clear that the impacts of climate change
will be felt through changes in natural ecosystems and the productivity of land. In order for human
settlements to build resilience to climate change investments need to be made into the land. Occupants of
land who do not have ownership or a secure access to their land are less likely to invest in the adaptive
capacity of the land. Overall, securing land tenure rights over land and natural resources is a critical feature
of building adaptive capacity (Quan & Dyer 2008). .
Communities with low levels of ownership compared to the national average scored higher on this indicator.
Map 11: Tenure status
Source: Calculated from Statistics SA Census 2011 data
2.4.
Composite Map of Adaptive Capacity
The composite map of adaptive capacity clearly shows that provinces with large rural human populations in
the Eastern Cape, Kwazulu-Natal, Mpumalanga, Limpopo and North-West Province are the most socially
vulnerable to climate change. At the same time, the economic contribution of agriculture in many of these
areas is relatively low, as are levels of income and employment. This suggests that community’s dependent
on subsistence farming and grants for survival are particularly socially vulnerable to climate change.
Map 12: Composite map showing social vulnerability
2.5.
Local vulnerability and planning
Spatially mapping vulnerability assists national policy makers in observing the patterns and trends that exist
within the country for the drivers of poverty and climate change vulnerability. Spatial maps assist in
identifying shared problems and in understanding the reasons behind these shared problems through the
observation of spatial patterns. A trend that consistently emerges from the spatial maps, that depict the
different components of vulnerability, is areas in and around the former homelands all share in similar
problems that lead to their heightened overall climate change vulnerability.
Social vulnerability is best measured as close to home as possible; therefore at the household or community
level. This is because vulnerabilities are unevenly distributed across societies and communities. This social
vulnerability assessment therefore aimed to provide a broad overview of the social vulnerabilities that rural
human settlements face; but more contextualized analyses are required to inform local planning.
2.6.
Stakeholder Engagement
As part of the consultation for the Climate Sector Adaptation Planning process, initial findings from the Risk
and Vulnerability Assessment were presented to stakeholders at sub-regional workshops held in
Johannesburg, Bloemfontein, Durban and Cape Town. Stakeholders from local municipalities, civil society,
NGO’s and research institutions were invited.Participants in the workshops were asked to perform a ranking
exercise in relation to the importance of a variety of climate change indicators and following this, to prioritise
adaptation responses in terms of a budgeting exercise. While the outcomes from these exercises should be
treated with caution, and in no way mitigate the need for local assessments of risks and vulnerability that
draw on deeper and broader community participation, the do provide an indication of current perceptions
amongst stakeholders of the relative importance of climate change risks.
The outcomes from the workshops are summarised in the table below (the workshop methodology was adjusted after the first workshop in Gauteng):
Johannesburg
Bloemfontein
Durban
Cape Town
Ranking of indicators/impacts in terms of perceived importance (Top 5)
Loss of agricultural productivity
Flood damage
Loss of surface water
Loss of agricultural productivity
Damage from veld fires
Loss of surface water
Loss of biodiversity
Disease
Flood damage
Loss of Biodiversity
Drivers of Social Vulnerability (top 5)
Access to water
Access to water
Land ownership
Housing
Income
Income
Access to health services
Access to energy
Access to sanitation
Age
Simple Daily Intensity (Precipitation)
Maximum daily temperatures
Number of warm days
Length of Growing Season
Very Wet Days
Other related threats
Soil erosion/land degradation, Acid Mine
Drainage, Mudslides/sink holes/dolomites,
Fire, Lightning
Tornadoes/hail storms, Agricultural pests,
Deforestation, Air and water
quality/pollution, Land use changes
Top Adaptation Priorities for Rural Human Settlements
 Facilitate the fast-tracking of
 Invest in capacity building and
service delivery with other
capital investment to build
departments, i.e. water,
resilient communities
sanitation, houses, health and
 Improve rural connectivity with
electricity.
enabling roads
 Provision of water harvesting and
 Improve early warning systems to
atmospheric water generation
cover rural communities
technologies to rural communities
 Fast-track land redistribution
 Make bursaries available to rural
process for agricultural
students for studies/disciplines
development
that will benefit rural human
 Provide finance to rural
settlements
community programmes for local
 Inculcate attitudes and practices
economic growth and building
which deal effectively with
resilient communities
change in general
 Infrastructure development
 Install water pipes to reservoirs
responding to disaster
and households and encourage
management
sustainable water use
 Research and make provision for
new agricultural



Sustainable Eco-friendly Housing
Infrastructure
Improve Veld Fire Management in
Rural Areas – education and
regulations
Provide Land to Local
Communities for Commercial
Farming – Subdividing Land into
Small Portions
Loss of surface water
Flood damage
Disease
Loss of agricultural productivity
Loss of Biodiversity
Access to water
Income
Access to sanitation
Housing
Access to health services




Build adaptive capacity to
respond to climate change
challenges
Initiate small business
development using environment
opportunities
Conduct community awareness
and education programme
Improve water quality
management at rural areas by
implementing sustainable
management practice

practices/techniques which
require less water usage
Strategic land acquisition and
disposal
3. Conclusion
Although South Africa already makes a substantial contribution to IPCC reports and has centres of
excellence in climate change research such as the Centre for Scientific Research (CSIR), South African
Weather Service, Water Research Commission (WRC), South African National Biodiversity Institute and
the University of Cape Town Climate Systems Analysis Group (UCT CSAG) that have the capacity to
develop and work with climate models; this is an area that should receive priority attention and funding. In
particular there is a need to systematically incorporate up-to-date modelling of atmospheric climate
change projections with sensitivity data to produce more sophisticated modelling of composite
environmental risks such as floods, droughts, and veld-fires. Improved earth observations are a critical
component of this effort (CCSP 2008).
Accurately downscaled models or models produced with regional climate data are an essential tool for
creating relevant knowledge for communities across South Africa, including rural communities, who are
often the least monitored for signs of risk and vulnerability. The most important aspect of regional climate
modelling is however not to predict the future but rather to elicit the information that is relevant for
different people in the very different regions across the country. Useful information for rural dwellers, for
example, would include the weather and climate impacts on agriculture. Armed with this type of
knowledge subsistence farmers could be informed as to which crops are most suitable to grow in a
specific season and over a certain time frame thereby drastically improving household resilience to a
range of livelihood stressors of which climate change is but only one. Relevant information derived from
regional climate models could further support the well-being of rural dwellers by equipping the
development of early warning systems that could inform people of advancing risk, such as flooding,
thereby providing people with the opportunity to use the resources at their disposal best to protect their
livelihoods.
This national assessment of environmental risk for rural human settlements served not as outline for
every region in South Africa, but rather as a broad framework under which more regional and local
planning should take place. This assessment of environmental risk for rural human settlement can be
used as tool for provincial and local governments as they embark on a process of understanding the
environmental risks for rural settlements within their own localities.
This risk and vulnerability assessment has outlined the most prominent risks for rural human settlements
in terms of hazard exposure and bio-physical sensitivity which both comprised a part of environmental
risk, and social vulnerability which is seen as deterrent to adaptive capacity. This national risk and
vulnerability assessment has been performed at a large scale and seeks to provide a broad framework for
the risk and vulnerabilities that rural communities across South Africa may face. More regional planning,
taking into account the local risks and vulnerabilities for rural inhabitants, is required in order to properly
address the contextualized nature of climate change vulnerability. There is an urgent need for further
research into improving the science of climate change in order to improve the risk and vulnerability
assessments that are derived from this science, particularity at the local level.
Further, in order for adaptation planning to be effective the scientific information form climate change
assessments need to be translated in a relevant manner to vulnerable communities so that determination
can be built in order to address climate risks. This need is most prevalent in the developing world where
climate change vulnerability is greatest and adaption planning will be the lease effective because of a lack
of knowledge, awareness and weak institutional structures. The organization ACCCA (Advancing
Capacity to Support Climate Change) recommend five key interventions towards effectively
communicating climate change risks.
1. Understanding the local decision making context for climate risk management and determining
the level of knowledge about climate risk is critical in selecting which knowledge is relevant for
communities.
2. Stakeholder engagement is critical in order to encourage social learning between decision
makers/project implementers and communities in order to determine which information is
relevant.
3. An active dialogue between policy makers and communities in crucial to ensure that the concerns
of the people are integrated into planning processes.
4. Interaction and learning by doing are crucial tools in bridging the gaps between climate science,
policy making and practise.
5. Climate risk messages must be clear and informed by a wide range of credible sources (ACCCA
2009).
Climate change adaptation is not necessarily about redeveloping the wheel; it is about making typical
developmentclimate resilient. One adaptation strategy for the Department of Rural Development and
Land Reform (DRDLR) as the national government office responsible for rural development can be then
to work climate resilience objectives into its already existing rural development programme.The purpose
of the departments rural development programme is to: “initiate, facilitate, coordinate and catalyse the
implementation of a Comprehensive Rural Development Programme (CRDP) that leads to sustainable,
equitable and vibrant rural communities. Rural Development is structured in two branches, the one is
responsible for Social, Technical, Rural Livelihoods and Institutional Facilitation (STRIF) and the other is
responsible for Rural Infrastructure Development (RID)” (DRDLR 2013).
The objectives of the DRDLRs Rural Development Programme are as follows:

To reach all the poorest rural wards in all rural municipalities by 2014.

To establish food gardens and Agriparks CRDP wards by 2014.

To skill and capacitate rural communities and land reform beneficiaries in technical enterprise
development trades.

To develop and utilize innovative service delivery models to enhance food production and ensure
food security.

To increase number of jobs created in CRDP initiatives by 2014.

To provide economic infrastructure in rural areas effectively reducing spatial inequalities by 5%.

To provide social infrastructure in rural areas effectively reducing spatial inequalities by 5%.

To provide ICT infrastructure in rural areas effectively reducing spatial inequalities by 5%.

To establish village agricultural industries and agricultural by 2014.

To establish and support Councils of Stakeholders, cooperatives and community based
institutions in each CRDP site by 2012.

To establish partnerships or economic productivity on CRDP sites.

To improve disaster management service in rural areas and land reform projects by 2012
(DRDLR 2013).
All of the objectives of the DRDLRs Rural Development Programme will automatically build on the
adaptation capacity of rural human settlements, however these efforts can be maximised when armed
with the knowledge of the anticipated climate risks. These strategies will further be addressed in the final
adaptation plan for rural human settlements in South Africa which is a result of a series of regional
stakeholder workshop consultations across South Africa in which this risk and vulnerability assessment
for rural human settlements was presented.
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http://europeandcis.undp.org/uploads/public1/files/Mapping%20CC%20Vulnerability%20publication%20%20November%202010.pdf.
Annexure I: Data Sources
The list below provides details of institutional sources of data and online portals that can be used by
climate change adaptation practitioners and local government to inform local adaption planning
Long Term Adaptation Scenarios
Website: http://www.sanbi.org.za
The LTAS is government’s central planning process for climate change adaptation, coordinated by the
South African National Biodiversity Institute (SANBI).
Statistics South Africa (StatsSA) Digital Census Atlas
Website: http://geoinfo.statssa.gov.za/censusdigitalatlas/Default.aspx
The 2012 Digital Census Atlas dissemination product is based on an interactive mapping principle where
the user selects the variable and level of geography for rendering through a web based interface. The
product has four main components namely Overview maps, Feature maps, Comparative maps and
Reporting. Overview maps display derived variable information as charts for provinces, district
municipalities and local municipalities. Feature maps allow the user to delve deeper into the Geographical
layers of South Africa in order to see the thematic breakdown of derived variables at lower geographical
levels. The reporting functionality allows the display of census variables in their specific categories per
selected geography. Comparative mapping will provide the functionality where selected variables can be
viewed simultaneously as thematic maps for all the censuses since 1996.
University of Cape Town Climate Systems Analysis Group (CSAG) Climate Information Portal
Website: http://cip.csag.uct.ac.za/webclient/map
CSAG make a number of interactive maps available through their online portal that provide access to
both weather data and long term climate projections based on downscaled GCMs and regional
observation stations.
South African Risk and Vulnerability Assessment (SARVA)
Website: http://www.sarva.org.za/
Through their online portal SARVA provide useful conceptual tools to support risk and vulnerability
assessments as well as a range of spatial maps of key indicators relating to sensitivity and social
vulnerability, and links to climate change reports from the CSIR, organised into themes.
CSIR Geospatial Analysis Platform (GAP)
Website: http://www.gap.csir.co.za/
The CSIR’s GAP platform provides access to spatial mappings of, amongst others, economic activity and
employment, population distribution, and climate change projections.
Agricultural Research Council and the Department of Agriculture, Forestry and Fisheries AGIS
(Agricultural Geo Referenced Information System)
Website: http://www.agis.agric.za/agisweb/agis.html
The ARC’s online portal provides access to both static and interactive maps relating to a large number of
indicators relevant to climate change adaptation in rural areas, including spatial maps of natural
resources, current land use, and agricultural potential for specific crops.
Annexure II: Social vulnerability indicators and ranking
Table 1 below provides a description of the indicators of social vulnerability used in this study, a brief
statement of the rationale for their selection, the data source used, and how the data was ranked to
produce spatial maps.
Table 1: Indicators for Social Vulnerability
Indicator Description
Rationale
Data Source
Ranking
Access
to
Services:
Combines data from Census
2011 on access to water and
sanitation,
energy,
and
waste collection.
Access to basic service
delivery
effects
the
resilience
of
communities in terms of
health and time available
for activities other than
providing
for
basic
needs.
StatsSA
–
Census 2011
Wards are given a score of 0 to 4
depending on the extent of their
access to basic services. Wards
with the least access are given a
rating of 4.
The weightings for the individual
census components are as follows:
Water: 40%
Energy: 25%
Sanitation: 25%
Waste: 10%
Household
Census 2011
income data.
Income:
household
Households with a low
level of income are less
likely to have access to
credit and less resilient
to all shocks – including
those associated with
climate change and its
impacts
on
health,
economy,
and
infrastructure.
StatsSA
–
Census 2011
Wards are given a score of 0 to 4
depending on income profiles.
Wards with the lowest income
profiles are given a rating of 4.
Ranked by Divided annual income
into groups:

None: 0

Low: 0 - R38 200

Medium: R38 201 R153 800

High: >R 153 800
Points
assigned
as
follows:
+1 where None > 10% (Nat avg =
15.1%)
+1 where Low > 20% (Nat avg =
48%)
+1 where Medium < 10% (Nat avg
=
22.2%)
+1 where High < 5% (Nat avg =
14.7%)
Indicator Description
Rationale
Data Source
Ranking
Severe malnutrition in
children under 5 years:
This indicator measures the
number of new cases of
children who weigh below
60% of their expected
weight-for-age per 1000
children
in
the
target
population which is children
under the age of five.
Areas where malnutrition
is
high
already
experience
food
insecurity due to a
variety of reasons such
as poverty and the
incapacity
to
grow
enough of the right kinds
of
food.
These
incapacities largely affect
rural communities who
experience
erratic
income flows and are
reliant on subsistence
farming. Climate change
will increase the already
existing barriers to food
security.
The
District
Health
Barometer
2010/11
by
the
Health
Systems Trust
of
South
Africa.
Data
obtained from
Statistics
South Africa;
2010.
A score from 1 to 5 will be assigned
to values in bands across the total
range of values.
Primary
health
utilisation rate
care
The primary health care
(PHC)
utilisation
rate
indicator
measures
the
average number of PHC
visits per person per year to
a public PHC facility. The
indicator is calculated by
dividing the total PHC
annual headcount by the
total catchment population.
South Africa is currently
aiming for a target of 3.5
PHC visits per person per
year.
Gender
of
head
of
household:
Percentage
males and female headed
households. This indicator is
used to identify wards with
significantly greater femaleheaded households than the
national average.
The Primary Health Care
Utilization rate provides
an indication of areas in
which people have or do
not have regular and
easy access to medical
care
from
medical
facilities. These medical
facilities are often far
apart in rural areas and
are often not as well
equipped. Since climate
change is likely bring
new
environmental
stressors
upon
the
health of communities
such as heat stress,
waterborne diseases and
increased malnutrition,
access to regular and
quality medical care will
enhances the adaptive
capacity
of
rural
settlements.
Female
headed
households are likely to
have less access to
resources and labour to
support climate change
adaptation. In many rural
areas
women
face
discrimination of various
sorts.
The
District
Health
Barometer
2010/11
by
the
Health
Systems Trust
of
South
Africa.
Data
obtained from
DHIS (2011)
A score of 5 will represent a low
adaptive capacity while a score of 1
will represent a high adaptive
capacity.
A score from 1 to 5 will be assigned
to values in bands across the total
range of values.
A score of 5 will represent a low
adaptive capacity while a score of 1
will represent a high adaptive
capacity.
StatsSA
–
Census 2011
Wards scored between 0 and 1.
1 point given to all wards in which
the percentage of female headed
households is 10% greater than the
national average
Indicator Description
Rationale
Data Source
Ranking
Population age profile:
Age provided in 5 year
intervals from 0 to 85+. This
indicator is used to identify
wards in which there are
significantly
above
the
national average children
and/or old people.
Areas with above the
national average children
and/or old people will
have less economically
active adults and are like
to be less resilient to
climate change.
StatsSA
–
Census 2011
Wards scored between 0 and 2.
Land ownership: Identifies
areas in which land is owned
privately, by the state, or
communally
It is more difficult to raise
capital against land that
is communally owned,
and
therefore
more
difficult to develop.
1. Divide ages into three groups:
0-14:
child
15-39:
young
40+:
old
2. Areas that are 10 percentage
points on either of side of the
national average are regarded as
extraordinarily low or extraordinarily
high.
3. Wards with a score of 1 has
either (high percentages of children
or low percentages of young
people) or (high percentages of old
people and low percentages of
young
people).
Wards with a score of 2 have few
young people and many old people
and many children.
Wards scored between 0 and 5 on
the basis of the percentage of the
ward that is communally owned.
Wards that are 75-100% communal
land are scored as 4.
Indicator Description
Rationale
Data Source
Ranking
Dwelling type: Stats SA
provides
the
following
categories:
The permanence and
strength
of
housing
directly influences its
vulnerability to extreme
weather events.
Stats
SA:
Census 2011
Each ward is assigned a score of 1
if the percentage of informal
housing is 5% above the national
average.

House
or
brick/concrete block
structure
on
a
separate stand /
yard or on a farm

Traditional
dwelling/hut/structur
e

Flat or apartment in
a block of flats

Cluster house
complex

Townhouse

Semi-detached
house/flat/room
backyard
in
in

Informal dwelling in
backyard

Informal dwelling in
an
informal
settlement or on a
farm

Room//servants
quarters/granny flat
Caravan/tent

Other