Socioeconomically Informed Use of Geostatistics to Track Adaptation of Resource-Poor Communities to Climate Change

Abstract

As the Green Climate Fund continues to make concerted efforts to leverage funding for resource-constrained communities in the global south under the aegis of increasing climate change impacts in sub-Saharan Africa, there is urgent and compelling need for tools that assist organizations to track the effectiveness of adaptation interventions in reducing vulnerability. This chapter offers a cost-effective methodology to track adaptation by using a case-study-based identification of communities with diminishing coping capacities in Raymond Mhlaba Local Municipality in the Eastern Cape Province of South Africa. Multistep geostatistical techniques were utilized in the ArcGIS 10.5 software environment to rank and spatialize changes in adaptation by using demographic census data for the years 2001 and 2011. Results of the analysis revealed that 12 communities had declining or static adaptive capacities between 2001 and 2011, while 10 communities had long-term decrease in adaptive capacities from 2001 to 2011 from a sampling universe of 134 communities. These findings are important because they demonstrate that the methodology can be effectively used to provide actionable information on the prevalence of low adaptation capacities at appropriate temporal and spatial scales, in order to guide the allocation of limited resources to the most deserving communities.

Linking Satellite, Land Capability, and Socio-Economic Data for Local-Level Climate-Change-Adaptive Capacity Assessments and Decision Support

“…The area's climate is characterized by average mid-day temperatures that range between 19.3 • C in winter and 28.3 • C in summer (www.statssa.gov.za, accessed on 6 May 2022). Most of the rainfall occurs in autumn, with annual amounts averaging 600 mm, with overall distribution ranging from 7 mm and 66 mm in July and March, respectively [15]. This distribution pattern poses significant challenges for the capacity of local communities to adjust to the increasing incidence of climate-change-driven rainfall failures and droughts.…”

“…The indicators for socio-economic adaptive capacity were deliberately chosen to ensure the inclusive representation of diverse communities as well as the unique geographical and socio-economic characteristics of their respective localities. The description, rationale, and ranking of demographic indicators (age profiles, income levels, literacy levels, and water source) that were used for assessing socio-economic adaptive capacity are provided by [15].…”

“…A Python script was utilized to automate the scoring of the levels of income attribute table and three indicators (namely, age profiles, levels of literacy, and water source) for the census years 2001 and 2011 (Supplementary Material-Python Scripts). The script underwent modifications to conform to the criteria outlined by [15] through the adjustment of table names, field names, and row numbers. The automated procedure entailed the generation of a new shapefile and attribute table, followed by the importation of the pre-calculated scores for each community that were derived from the four indicators, as illustrated in Table 2.…”

“…In this summarization, scores ranging from 1 to 5 were classified as low, those ranging from 6 to 10 were classified as medium, and the scores ranging from 11 to 15 were classified as high. This analysis was further boosted by appending an additional attribute field to the table, which accommodated the adaptive capacity scores that were ranked following procedures suggested by [15].…”

“…A map of adaptive capacity was compiled in Arc Map software platform by utilizing the rankings derived from the data of 2001 and 2011 censuses. A Python script [15] was utilized to generate a new shapefile in order to record adaptive capacities that were derived from the indicator scores tables for the years 2001 and 2011 in a unified attribute table (Table 3). The following formula was utilized for computing the alterations in adaptive capacity scores between the census years 2001 and 2011, as illustrated in Table 3.…”