Assessing Social Vulnerability to Climate Change: A Case from Ghana

Susan Charnley and Sophia PolaskyUSDA Forest Service, Pacific Northwest Research Station

Purpose of assessment:

Nationwide climate change assessment to help USAID Ghana incorporate climate change into their programming Climate models predicting how the

climate will change Assessments of social and natural resource

vulnerability to climate change Overview of current and potential adaptation

strategies (not adaptive capacity) Overview of mitigation activities (REDD+, CDM)Feed the Future program recommendations($42 M/yr for 5 years, Global Hunger & Global

Food Security Initiative)

Two-Tiered Approach

Overview of climate change predictions and vulnerability at the national level

Targeted review of adaptation and mitigation measures underway in the regions of Ghana most of interest to USAID (northern and coastal areas)

Four step process for constructing a vulnerability index – “indices & indicators” approach

1 – Identify variables that contribute to social vulnerability to climate change based on the literature

2 – Select SE indicators that serve as proxies for evaluating these variables at the district level

3 – Scale the indicator values and combine them into one index to measure overall social vulnerability to climate change across districts

4 – Map the values generated by the index using GIS, and make comparisons

Methods: Steps 1 & 2

Reviewed the literature Chose scale of analysis: Ghana has 10

administrative regions and 110 districts 11 indicators chosen as proxy measures

for vulnerability Data gathered at the district level for

each indicator (using data provided by the Ghana Statistical Service)

Methods: Step 3

For each indicator, districts were grouped into 10 data classes based on natural statistical breaks in their indicator values (the Jenks method).

Districts received scores based on their grouping for each indicator. Least vulnerable class = 1, most vulnerable class = 10.

Each district received 11 scores, 1 for each indicator.

Indicators were equally weighted.

Methods: Steps 3 & 4

The 11 scores were added for each district to come up with an overall index score. (They ranged from 25 to 93 out of a possible 11 to 110.)

Each indicator score was mapped by district, as was the composite social vulnerability index score, geo-spatially, using ArcGIS software.

Vulnerability maps were then overlaid with regional boundaries and ecological zone boundaries.

Natural resource vulnerability measures not included in the index; we used them to contextualize findings

Indicator Description

Ability to survive crisis

% of total district households that felt “somewhat insecure” or “very insecure” about their ability to withstand any crisis.

Agricultural employment

% of the district’s total population (over 15 years of age) engaged in agricultural related employment.

Dependent Population

% of a district’s total population <15 and >65 years of age.

Indicator Description

Distance from drinking water

% of total district households that travel 30 min. or more for drinking water

Distance from food market

% of total district households that travel 30 min. or more to reach a food market

Female-headed households

% of total district households headed by a female

Illiteracy % of total district population >15 years of age that is illiterate

Indicator Description

Malnourished children

% of children <5 years old within a district that are underweight for their age

Poverty Perception

% of total district households that self-identify as “poor” or “very poor”

Road accessibility

% of district households that can access their homes by road year-round

Unimproved drinking water

% of total district households that depend on unimproved sources for drinking water (e.g., rainwater, rivers, lakes, ponds, and unprotected wells)

Percentage of the district’s households that travel 30 minutes or more to reach a food market

Percentage of the district’s population, over 15 years of age, that is illiterate.

The percentage of total district households that expressed feeling “somewhat ” or “very” insecure about their ability to withstand any crisis.

Composite vulnerability index (Ghana’s 10 administrative regions are superimposed over districts)

Composite vulnerability index (Ghana’s six different ecological zones are superimposed over districts)

Poverty by Region

Administrative Region 1991/92 1998/99 2005/06Western 60 27 18Central 44 48 20Greater Accra 26 5 12Volta 57 38 31Eastern 48 44 15Ashanti 41 28 20Brong-Ahafo 65 36 29Northern 63 69 52Upper East 67 88 70Upper West 88 84 88Ghana 52 40 29

Climate

Rainfall Temperature

9921115.3

1301.3 1303.4

2092.7

890.2

0

500

1000

1500

2000

2500

Sudan

Savanna

Guinea

Savanna

Transition Deciduous

Forest

Evergreen

Forest

Coastal

Savanna

Mean Annual Precipitation (mm)

28.6

27.5

27

26.4 26.4

26.9

25

25.5

26

26.5

27

27.5

28

28.5

29

Sudan Savanna

Guinea Savanna

Transition Deciduous Forest

Evergreen Forest

Coastal Savanna

Mean Annual Temperature (C)

Natural resource vulnerability

Adaptation: New Technologies

Left: Fuel efficient cook stoves use less wood to prepare meals, and save women from having to spend an increasing proportion of their day seeking firewood.Above: Mavis and Julianna Effah spend their evenings learning computer skills.

Adaptation: Livelihood Diversification

Left: Man weaves kente cloth for sale to tourists.Above: Woman sells hand crafted beads at the Koforidua bead market, a growing tourism destination.

Adaptation: Agricultural Intensification

Left: Mr. Peter Peprah, of Wamfie, Ghana, displays seedling starts which he will plant on his family’s farm as part of a new permaculture based system. He hopes this new system will both diversify his production and increasing the health of his land for stable production over the long term.

Adaptation: Agricultural Extensification

Adaptation: Migration

Methodological Strengths

The approach can be scaled up or scaled down, depending on the scale of interest, as long as existing data are available

Facilitates integration with biophysical data Allows rapid assessment of most vulnerable

areas to target where in-depth, local-level research on climate change social vulnerability at the household and community levels can focus

Points to places where investments to enhance adaptive capacity and resilience might be targeted

Appealing to managers & decisionmakers

Methodological Weaknesses

It provides a general assessment that alone is insufficient for fully understanding the nature of social vulnerability as it exists locally

It is limited in scale and time period by the available data

Some dimensions of social vulnerability are hard to get at using existing indicators (ie, rights & empowerment, cultural variables)

Data quality can be questionable Risk of overlooking communities & households

in districts that score low in vulnerability

Thank you!