Climate change is expected to impact biodiversity, global temperatures and the water cycle resulting in changes that affect communities in the social system. The capacity to adapt to such climatic fluctuations will vary across social groups, depending on the group’s socio-economic and demographic characteristics. This thesis constructs a methodology that bridges existing approaches to study adaptation, providing an alternative that includes high level deterministic approaches to quantify adaptive capacity (AC) and case-by-case strategies to enhance region-specific AC. The proposed methodology consists of an exploratory model and a principal component analysis. The goal of the methodology is to create a sub-national level AC characterization to prioritize domestic policy within a country and to understand the most important factors affecting a region’s AC. In addition, the methodology incorporates an important challenge of developing countries: collection, processing, and use of information. To illustrate the methodology, a case study focused on three regions of Peru: Ayacucho, Loreto and Piura. The results of the methodology show that Ayacucho, a region facing low temperatures and glacier retreat, has the lowest AC and thus has priority in the AC strengthening process. The main factors affecting Ayacucho’s AC include infrastructure, poverty and low economic capacity. Loreto, with changes in biodiversity and ambient temperatures, has the second highest AC to climate change as a result of a diverse population and low literacy levels. To increase Loreto’s AC more transcultural education and family planning might be required. Finally, Piura has the highest AC due to its high economic capacity, but this is affected by the region’s social inequality. Minimizing poverty is a critical measure to support and increase AC of this region, which faces increased precipitation, flooding and disease contraction and contagion.