By Martin M. Chari, Hamisai Hamandawana and Leocadia Zhou
This article presents a case study-based approach that was used to identify resource-poor communities with limited abilities to cope with the adverse effects of climate change. The study area was a part of Raymond Mhlaba Local Municipality (formerly Nkonkobe Local Municipality) in the Eastern Cape, which is one of South Africa’s provinces ranked as being extremely vulnerable to the adverse effects of climate change due to high incidences of poverty and limited access to public services such as water and education. Although adaptive capacity and vulnerability assessments help to guide policy formulation and implementation by identifying communities with low coping capacities, policy implementers often find it difficult to fully exploit the utility of these assessments because of difficulties in identifying vulnerable communities. This article attempts to bridge this gap by providing a user-friendly, replicable, practically implementable and adaptable methodology that can be used to cost-effectively and timeously identify vulnerable communities with low coping capacities.
A geostatistical approach was used to assess and evaluate adaptive capacities of resource-poor communities in the Nkonkobe Local Municipality. The geospatial component of this approach consisted of a multi-step Geographical Information Systems (GIS)-based technique that was improvised to map adaptive capacities of different communities. The statistical component used demographic indicators comprising literacy levels, income levels, population age profiles and access to water to run automated summation and ranking of indicator scores using GIS software (Figure 1) to produce maps that show spatial locations of communities with varying levels of adaptive capacities on a scale ranging from low, medium to high.
From a total of 180 villages in the Nkonkobe Local Municipality, the analysis identified 14 villages with low adaptive capacities (Figure 2). This finding is important because it suggests that the methodology can be effectively used to objectively identify communities that are vulnerable to climate change.
The tool could be used for targeting assistance to climate change vulnerable communities. The methodology proposed is of general applicability in guiding public policy interventions aimed at reaching, protecting and uplifting socio-economically disadvantaged populations in both rural and urban settings.
When methodology is implemented in a wider context such as at provincial level, the results are extremely helpful in guiding the formulation of provincial or national climate change response strategies and development action plans. Overall, results produced by using this methodology at any spatial extent assist to fulfil the objective of the National Climate Change Response Plan White Paper (NCCRP) of South Africa. This objective is to boost climate change adaptive capacity and effectively manage inevitable climate change impacts through interventions that build and sustain South Africa’s social, economic and environmental resilience and emergency response capacity using cost-effective and implementable methodologies. Government, research institutions and civil societies are therefore encouraged to use the methodology with available datasets to map adaptive capacity in rural Eastern Cape and other rural areas of South Africa.
The approach’s ability to identify vulnerable communities is useful because it aids the identification of resource-poor communities that deserve priority consideration when planning and implementing adaptation activities that deliver support and assistance to those least capable of effectively coping with the adverse effects of climate change induced vulnerabilities.
The authors are researchers from Risk & Vulnerability Science Centre (RVSC) at University of Fort Hare. This research is part of the Water Research Commission (WRC) – project K5/2496/4 on integrated use of seasonal forecast for community preparedness to climate variability which is being led by Dr Olivier Crespo from Climate Systems & Analysis Group (CSAG) at University of Cape Town.