## 4 Discussion
Despite these, it is crucial to observe that the two proxies selected for this current study cut across all other proxies, and to avoid collinearity, we focus on these two proxies. In their assessment of a framework for climate change adaptation, [PERSON] and [PERSON] [28] note that adaptation readiness captures adaptive capacity, which exhibits the strength and existence of governance and policy structures that are responsible for whether adaptation is taking place or not. The drivers of adaptive capacity identified in the latter study create an environment suitable for adaptation, and indeed, they affect the two proxies identified in this study. For example, governance and policies play an essential role in people's poverty and literacy rates as safety nets put in place by the government through investments in education and poverty alleviation can invariably impact society's readiness for climate change adaptation. [PERSON] and [PERSON] [16] also used a similar approach in which they examined governance mechanisms and policy processes that could contribute to joint adaptation and economic planning, leadership, institutional mechanisms, science-policy nexus, decision-making structures, stakeholder involvement, and technological innovation. An important observation from this study is that the readiness/ClimAdaptCap Index ranges from around 0.35-0.39, indicating that there is a very slight variation in the index. In addition, the level of readiness is generally low for Africa. This is evident as the regions that perform best such as North and Southern Africa, record scores of about 0.39 when compared to a maximum possible score of 1. This low variation between regions and the generally low performance in the context of readiness across Africa is an indication that adaptation efforts need to be enhanced across Africa. In addition, West Africa records the lowest readiness scores in Africa, and among other things, this can be explained by the fact that this region witnesses some of the highest rates of deforestation in Africa. In the presence of high temperatures and low precipitation levels, North Africa still records the highest scores, and this is partly explained by the relatively higher literacy rates and lower poverty rates. The readiness index for climate change adaptation (ClimAdaptCap Index) developed by this investigation was developed mainly to evaluate and assess readiness for climate change adaptation within an African context. However, a major advantage that it has is that it can be adjusted to the realities of other regions of the world. The proxies of adaptive capacity might not be the same for non-African regions. The strength of this index is that it can integrate any adaptive capacity proxy. In fact, this index is known for its ability to simulate readiness based on any climatic and socioeconomic variables of interest under a given condition. The ability of the index to simulate both climatic and non-climatic variables brings the results very close to reality as often people are adapting to climate change. This new index will play a major role in adaptation mapping in Africa and in other parts of the world. This is evident as it provides an opportunity for various stakeholders to have discussions around the different options that can be used to enhance adaptive capacity and, therefore, readiness across Africa. The weaknesses of the index are that it focuses on only four indicators of readiness (precipitation, temperature, literacy, and poverty rates), all the indicators have equal weighting, and a plethora of several other potential indicators exist but cannot be used due to the difficulties associated with the quantification of proxies of adaptive capacity and the absence of reliable data. Most of the climate data used are based on major well-known climate data repositories such as the World Bank Climate Portal, which is informed by the CIMIP5 modeling initiatives. The absence of adequate real-time, reliable weather station data across Africa is also an issue.